Self Conceptualizing Cognitive Graph: Bio-Inspired Self-Organizing Knowledge Graph for LLMs

Overview

CogniGraph is a groundbreaking framework that enables Large Language Models (LLMs) to autonomously conceptualize and organize their knowledge into dynamic, evolving knowledge graphs. By combining state-of-the-art language models with biologically-inspired mechanisms, CogniGraph creates a self-organizing conceptual network that mimics how living systems adapt, evolve, and self-regulate.

This project aims to address key challenges in LLM deployment:

• Reducing model size requirements by externalizing knowledge representation
• Decreasing inference power consumption through more efficient knowledge access
• Increasing inference speed by optimizing conceptual pathways
• Improving model adaptability with dynamic knowledge structures

Key Features

🧠 Autonomous Knowledge Extraction

• Self-directed concept exploration and discovery
• Extraction of concepts and relationships from model activations
• Dynamic concept development based on usage patterns

🧬 Bio-Inspired Mechanisms

• Immune System Metaphor: Concepts adapt and respond like antibodies to antigens
• Gene Regulation Network: Concept activation patterns that regulate each other
• Homeostatic Plasticity: Self-regulation of concept importance to maintain balance

📊 Neo4j Graph Database Integration

• Persistent storage of concept networks
• Advanced graph algorithms for similarity detection
• Community detection for concept clustering

🌱 Continuous Evolution

• Dynamic merging and splitting of concepts based on semantic similarity
• Automated pruning of inactive concepts
• Formation of hierarchical relationships

Architecture

CogniGraph consists of five core components:

1. ConceptNetworkManager: Orchestrates the entire system, managing data flow between components
2. ConceptExtractor: Analyzes model activations and text to identify meaningful concepts
3. BioInspiredNetwork: Implements biological computing metaphors for network adaptation
4. DynamicConceptEvolver: Handles the evolution and adaptation of concepts over time
5. SelfExplorer: Autonomously explores and expands the knowledge graph

Architecture Diagram

Installation

Prerequisites

• Python 3.8+
• Neo4j 5.x with APOC and Graph Data Science libraries
• At least 16GB RAM recommended

Setup

1. Clone the repository:

git clone https://github.com/yourusername/selforg.git
cd selforg

2. Install dependencies:

pip install -r requirements.txt

3. Set up a Neo4j database:

   • Install and start Neo4j (5.26.4 recommended)
   • Create a new database named selforg
   • Install APOC and Graph Data Science plugins
   • Set password to match config or update config.json

4. Prepare your model:

   • Download a compatible model (tested with granite_3.2_8b_model)
   • Update the model path in config.json

Configuration

The system can be configured through config.json:

{
    "neo4j": {
        "uri": "bolt://localhost:7687",
        "user": "neo4j",
        "password": "NewPassword123",
        "database": "selforg"
    },
    "model": {
        "path": "granite_3.2_8b_model",
        "load_in_8bit": false,
        "device_map": null
        // Other model settings...
    },
    "gds": {
        "similarity_threshold": 0.6,
        "community_detection": {
            "algorithm": "louvain",
            "min_community_size": 3
        }
        // Other graph algorithm settings...
    }
}

Usage

Basic Usage

Run the main script:

python run_kg.py

This will:

1. Load the configured model and database connections
2. Start the self-exploration process
3. Provide an interactive command interface

Interactive Commands

• status: Show exploration status and concept count
• start: Start self-exploration process
• stop: Stop self-exploration process
• explore <concept>: Explore a specific concept and its neighborhood
• exit: Close connections and exit

Advanced Options

python run_kg.py --config custom_config.json --max-concepts 2000 --no-auto-explore

• --config: Specify a custom configuration file
• --max-concepts: Set maximum number of concepts to explore
• --no-auto-explore: Disable automatic self-exploration on startup

How It Works

Concept Extraction

The ConceptExtractor identifies meaningful concepts from text and model activations using three approaches:

1. Attention Analysis: Extracting concepts from attention patterns in the model
2. Linguistic Patterns: Identifying named entities, technical terms, and acronyms
3. Activation Patterns: Detecting concepts from neuron activation strength

Interaction Flow

Processing Steps

Biologically-Inspired Adaptation

The BioInspiredNetwork implements three biological computing metaphors:

Immune System

Concepts act like antibodies that recognize and respond to new information. Similar concepts strengthen connections, while dissimilar ones may trigger new concept formation.

def immune_response(self, concept_id: str, similar_concepts: List[Dict[str, Any]]):
    # Increase affinity for similar concepts (clonal selection)
    for similar in similar_concepts:
        if similarity > self.affinity_threshold:
            # Create memory for this concept...

Gene Regulation

Concepts regulate each other's activation patterns, forming complex feedback loops that maintain memory and relevance.

Homeostatic Plasticity

The system self-regulates to maintain balance, preventing concept dominance or extinction.

def homeostatic_regulation(self):
    # Calculate network-wide statistics
    mean_activation = np.mean(activations)
    std_activation = np.std(activations)
    
    # Apply homeostatic regulation
    if abs(deviation) > std_activation:
        regulation = -deviation * self.decay_rate
        memory.activation = max(0, min(1, memory.activation + regulation))

Dynamic Evolution

The DynamicConceptEvolver handles concept lifecycle:

1. Merging: Similar concepts may merge into higher-level abstractions
2. Splitting: Concepts that represent multiple distinct ideas may split
3. Hierarchical Organization: Formation of parent-child relationships

Self-Exploration

The SelfExplorer drives autonomous knowledge discovery:

1. Generates seed concepts to begin exploration
2. Explores related concepts through targeted prompts
3. Infers relationships and connection types
4. Adds new concepts to the exploration queue

Exploration States

Performance Optimization

CogniGraph implements several optimizations for efficient operation:

1. CPU-only operation mode: Functions without GPU requirements
2. Low memory usage: Configured for minimum RAM consumption
3. Efficient graph algorithms: Using Neo4j GDS for scalable graph operations
4. Throttled exploration: Controlled concept discovery to prevent resource exhaustion

Future Directions

• Integration with multi-modal models for visual concept learning
• Distributed concept exploration across multiple models
• Fine-tuning capabilities using the generated knowledge graph
• Edge computing optimizations for embedded systems

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• This project draws inspiration from immune system computing, neural plasticity research, and gene regulatory networks
• Special thanks to the Neo4j and HuggingFace teams for their excellent libraries
• Built with IBM's Granite 3.2 8B parameter reasoning model

Contact

For questions, feedback, or collaboration opportunities, please reach out:

• Website: Frontier Tech Strategies
• Email: chris@frontiertechstrategies.com
• LinkedIn: Christopher Royse