Fractal Identity Matrix POC
This project demonstrates the integration of the Fractal Identity Matrix (FIM) principles into a Language Model (LLM) to optimize problem space subdivision and enhance interpretability and efficiency.
- Ensure you have Python 3.12 installed.
- Install the required Python packages using pip:
pip install -r requirements.txt
- Set your OpenAI API key in a
.envfile:OPENAI_API_KEY=your_openai_api_key_here
To run the main script, follow these steps:
-
Navigate to the LanguageAgentTreeSearch Directory:
cd LanguageAgentTreeSearch/programming -
Activate the Bash Environment: Ensure your terminal is set to use bash. If you're using a different shell, you may need to switch to bash:
bash
-
Execute the Shell Script: Run the provided shell script
run_lats_gpt4.shto set up the environment and execute the Python script:sh run_lats_gpt4.sh
This script will execute the Python script with the necessary environment variables set.
The output demonstrates how the LLM uses the FIM principles to organize data into a hierarchical structure. Each category and subcategory is assigned a similarity weight, which reflects the LLM's proprioception—its internal sense of how different parts of the problem space relate to each other.
- Working Proprioception: The LLM's ability to dynamically adjust and optimize the problem space based on similarity weights approximates working proprioception. This allows the model to efficiently allocate cognitive resources, enhancing interpretability and trust.
- Matrix Visualization: The adjacency matrix visualizes these relationships, showing how categories are interconnected. This structure helps reduce unnecessary data processing, lowering HPC costs and improving performance.
By focusing on cognitive economy and interpretability, the system moves away from the traditional 'black box' model, fostering transparency and user trust.
For more details on the implementation and the underlying principles, please refer to the FIM White Paper.