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research/theoretical_framework.md
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research/theoretical_framework.md
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# Theoretical Framework: Expert-Augmented LLM Ideation
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## The Core Problem: LLM "Semantic Gravity"
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### What is Semantic Gravity?
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When LLMs generate creative ideas directly, they exhibit a phenomenon we term "semantic gravity" - the tendency to generate outputs that cluster around high-probability regions of their training distribution.
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```
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Direct LLM Generation:
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Input: "Generate creative ideas for a chair"
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LLM Process:
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P(idea | "chair") → samples from training distribution
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Result:
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- "Ergonomic office chair" (high probability)
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- "Foldable portable chair" (high probability)
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- "Eco-friendly bamboo chair" (moderate probability)
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Problem:
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→ Ideas cluster in predictable semantic neighborhoods
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→ Limited exploration of distant conceptual spaces
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→ "Creative" outputs are interpolations, not extrapolations
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```
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### Why Does This Happen?
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1. **Statistical Pattern Learning**: LLMs learn co-occurrence patterns from training data
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2. **Mode Collapse**: When asked to be "creative," LLMs sample from the distribution of "creative ideas" they've seen
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3. **Relevance Trap**: Strong associations dominate weak ones (chair→furniture >> chair→marine biology)
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4. **Prototype Bias**: Outputs gravitate toward category prototypes, not edge cases
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---
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## The Solution: Expert Perspective Transformation
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### Theoretical Basis
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Our approach draws from three key theoretical foundations:
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#### 1. Semantic Distance Theory (Mednick, 1962)
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> "Creative thinking involves connecting weakly related, remote concepts in semantic memory."
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**Key insight**: Creativity correlates with semantic distance. The farther the conceptual "jump," the more creative the result.
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**Our application**: Expert perspectives force semantic jumps that LLMs wouldn't naturally make.
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```
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Without Expert:
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"Chair" → furniture, sitting, comfort, design
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Semantic distance: SHORT
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With Marine Biologist Expert:
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"Chair" → underwater pressure, coral structure, buoyancy, bioluminescence
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Semantic distance: LONG
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Result: Novel ideas like "pressure-adaptive seating" or "coral-inspired structural support"
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```
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#### 2. Conceptual Blending Theory (Fauconnier & Turner, 2002)
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> "Creative products emerge from blending elements of two input spaces into a novel integrated space."
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**The blending process**:
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1. Input Space 1: The target concept (e.g., "chair")
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2. Input Space 2: The expert's domain knowledge (e.g., marine biology)
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3. Generic Space: Abstract structure shared by both
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4. Blended Space: Novel integration of elements from both inputs
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**Our application**: Each expert provides a distinct input space for systematic blending.
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```
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┌─────────────────┐ ┌─────────────────┐
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│ Input 1 │ │ Input 2 │
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│ "Chair" │ │ Marine Biology │
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│ - support │ │ - pressure │
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│ - sitting │ │ - buoyancy │
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│ - comfort │ │ - adaptation │
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└────────┬────────┘ └────────┬────────┘
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│ │
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└───────────┬───────────┘
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▼
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┌─────────────────────┐
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│ Blended Space │
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│ Novel Chair Ideas │
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│ - pressure-adapt │
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│ - buoyant support │
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│ - bio-adaptive │
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└─────────────────────┘
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```
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#### 3. Design Fixation Breaking (Jansson & Smith, 1991)
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> "Design fixation is blind adherence to initial ideas, limiting creative output."
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**Fixation occurs because**:
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- Knowledge is organized around category prototypes
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- Prototypes require less cognitive effort to access
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- Initial examples anchor subsequent ideation
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**Our application**: Expert perspectives act as "defixation triggers" by activating non-prototype knowledge.
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```
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Without Intervention:
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Prototype: "standard four-legged chair"
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Fixation: Variations on four-legged design
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With Expert Intervention:
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Archaeologist: "Ancient people sat differently..."
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Dance Therapist: "Seating affects movement expression..."
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Fixation Broken: Entirely new seating paradigms explored
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```
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---
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## The Multi-Expert Aggregation Model
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### From "Wisdom of Crowds" to "Inner Crowd"
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Research shows that groups generate more diverse ideas because each member brings different perspectives. Our system simulates this "crowd wisdom" through multiple expert personas:
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```
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Traditional Crowd:
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Person 1 → Ideas from perspective 1
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Person 2 → Ideas from perspective 2
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Person 3 → Ideas from perspective 3
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Aggregation → Diverse idea pool
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Our "Inner Crowd":
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LLM + Expert 1 Persona → Ideas from perspective 1
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LLM + Expert 2 Persona → Ideas from perspective 2
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LLM + Expert 3 Persona → Ideas from perspective 3
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Aggregation → Diverse idea pool (simulated crowd)
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```
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### Why Multiple Experts Work
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1. **Coverage**: Different experts activate different semantic regions
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2. **Redundancy Reduction**: Deduplication removes overlapping ideas
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3. **Diversity by Design**: Expert selection can be optimized for maximum diversity
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4. **Diminishing Returns**: Beyond ~4-6 experts, marginal diversity gains decrease
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---
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## The Complete Pipeline
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### Stage 1: Attribute Decomposition
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**Purpose**: Structure the problem space before creative exploration
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```
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Input: "Innovative chair design"
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Output:
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Categories: [Material, Function, Usage, User Group]
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Material: [wood, metal, fabric, composite]
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Function: [support, comfort, mobility, storage]
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Usage: [office, home, outdoor, medical]
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User Group: [children, elderly, professionals, athletes]
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```
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**Theoretical basis**: Structured decomposition prevents premature fixation on holistic solutions.
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### Stage 2: Expert Team Generation
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**Purpose**: Assemble diverse perspectives for maximum semantic coverage
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```
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Strategies:
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1. LLM-Generated: Query-specific, prioritizes unconventional experts
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2. Curated: Pre-selected high-quality occupations
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3. External Sources: DBpedia, Wikidata for broad coverage
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Diversity Optimization:
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- Domain spread (arts, science, trades, services)
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- Expertise level variation
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- Cultural/geographic diversity
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```
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### Stage 3: Expert Transformation
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**Purpose**: Apply each expert's perspective to each attribute
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```
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For each (attribute, expert) pair:
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Input: "Chair comfort" + "Marine Biologist"
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LLM Prompt:
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"As a marine biologist, how might you reimagine
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chair comfort using principles from your field?"
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Output: Keywords + Descriptions
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- "Pressure-distributed seating inspired by deep-sea fish"
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- "Buoyancy-assisted support reducing pressure points"
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```
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### Stage 4: Deduplication
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**Purpose**: Ensure idea set is truly diverse, not just numerous
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```
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Methods:
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1. Embedding-based: Fast cosine similarity clustering
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2. LLM-based: Semantic pairwise comparison (more accurate)
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Output:
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- Unique ideas grouped by similarity
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- Representative idea selected from each cluster
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- Diversity metrics computed
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```
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### Stage 5: Novelty Validation
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**Purpose**: Ground novelty in real-world uniqueness
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```
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Process:
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- Search patent databases for similar concepts
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- Compute overlap scores
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- Flag ideas with high existing coverage
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Output:
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- Novelty score per idea
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- Patent overlap rate for idea set
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```
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---
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## Testable Hypotheses
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### H1: Semantic Diversity
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> Multi-expert generation produces higher semantic diversity than single-expert or direct generation.
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**Measurement**: Mean pairwise cosine distance between idea embeddings
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### H2: Novelty
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> Ideas from multi-expert generation have lower patent overlap than direct generation.
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**Measurement**: Percentage of ideas with existing patent matches
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### H3: Expert Count Effect
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> Semantic diversity increases with expert count, with diminishing returns beyond 4-6 experts.
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**Measurement**: Diversity vs. expert count curve
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### H4: Expert Source Effect
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> LLM-generated experts produce more unconventional ideas than curated/database experts.
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**Measurement**: Semantic distance from query centroid
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### H5: Fixation Breaking
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> Multi-expert approach produces more ideas outside the top-3 semantic clusters than direct generation.
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**Measurement**: Cluster distribution analysis
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---
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## Expected Contributions
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1. **Theoretical**: Formalization of "semantic gravity" as LLM creativity limitation
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2. **Methodological**: Expert-augmented ideation pipeline with evaluation framework
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3. **Empirical**: Quantitative evidence for multi-expert creativity enhancement
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4. **Practical**: Open-source system for innovation ideation
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---
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## Positioning Against Related Work
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| Approach | Limitation | Our Advantage |
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|----------|------------|---------------|
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| Direct LLM generation | Semantic gravity, fixation | Expert-forced semantic jumps |
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| Human brainstorming | Cognitive fatigue, social dynamics | Tireless LLM generation |
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| PersonaFlow (2024) | Research-focused, no attribute structure | Product innovation, structured decomposition |
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| PopBlends (2023) | Two-concept blending only | Multi-expert, multi-attribute blending |
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| BILLY (2025) | Vector fusion less interpretable | Sequential generation, explicit control |
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