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Contributing to KistMat AI

Thank you for your interest in contributing to KistMat AI! This document outlines the priority areas for improvement and how you can help.

Priority Areas for Improvement

1. Enhancement of Reasoning and Concept Understanding

  • Implement advanced natural language processing techniques for better comprehension of mathematical problems.
  • Develop a more robust symbolic reasoning module to handle abstract mathematical concepts.

2. Optimization of Data Loading and Problem Generation

  • Implement a DynamicProblemGenerator to generate problems on-demand, improving memory efficiency.
  • Create create_dynamic_dataset to integrate dynamic problem generation with tf.data.
  • Modify smooth_curriculum_learning to use dynamic datasets, allowing real-time adjustments of difficulty.

3. Improvement of Memory Usage (High Priority)

  • Optimize the external memory mechanism for more efficient use of computational resources.
  • Implement pruning and quantization techniques to reduce model size without significantly sacrificing performance.
  • Develop a more sophisticated memory management system to handle complex mathematical concepts efficiently.

4. Training Methodology Enhancement (High Priority)

  • Implement advanced curriculum learning strategies with dynamic difficulty adjustment.
  • Develop a hybrid training approach combining supervised learning with reinforcement learning for problem-solving strategies.
  • Introduce meta-learning techniques to improve the model's ability to learn new mathematical concepts quickly.

5. Model Usage on CPU and GPU (High Priority)

  • Optimize GPU detection and usage.
  • Improve CPU usage to utilize all available cores.
  • Ensure compatibility and transferability between CPU and GPU.

6. Code Modularization and Maintenance Improvement (High Priority)

  • Refactor the codebase into smaller, more manageable components.
  • Create separate modules for problem generation, model architecture, training loops, and evaluation metrics.
  • Implement a plugin architecture to allow easy addition of new mathematical concepts and problem types.

7. Expansion to Visual Tasks

  • Implement a Convolutional Neural Network (CNN) for processing mathematical image tasks.
  • Develop methods to extract and analyze activations from intermediate CNN layers.
  • Create a sparse autoencoder to decompose activations and identify visual patterns in mathematical notations.

8. Advanced Pattern Recognition

  • Implement visual attention techniques to identify key elements in visually presented mathematical problems.
  • Develop a mathematical symbol recognition system to interpret handwritten equations.

9. Model Behavior Manipulation

  • Experiment with artificial modification of activations to alter model behavior in problem-solving.
  • Develop methods to control model perception by manipulating specific components.

10. Enhanced Visualization

  • Create advanced techniques to visualize learned concepts across different mathematical domains.
  • Implement tools for visualizing "polysemantic neurons" in mathematical contexts.

11. Interpretability Enhancements

  • Develop interpretable regularization techniques.
  • Implement mechanisms to track neuron evolution during training.
  • Create tools for gradient analysis to better understand feature importance in problem-solving.

12. Robustness Testing

  • Develop a suite of tests to evaluate model robustness against various types of manipulations.

How to Contribute

  1. Review open issues or create a new one if you identify a problem or improvement not already listed.
  2. Fork the repository and create a branch for your contribution.
  3. Make your changes, ensuring you follow the project's style guidelines.
  4. Add or update unit tests as necessary.
  5. Update documentation related to your changes.
  6. Submit a pull request with a clear description of your changes and their purpose.

Coding Guidelines

  • Follow Python style conventions (PEP 8).
  • Add docstrings to classes and methods.
  • Include inline comments for complex logic.
  • Explain algorithms and important decisions in comments.

Testing

  • Create unit tests for new features or changes.
  • Ensure all tests pass before submitting a pull request.

Documentation Guidelines

  • Ensure all new features and modules are documented.
  • Add usage examples for new features.
  • Update existing documentation to reflect changes in the codebase.
  • Use clear and concise language.
  • Follow the existing documentation structure and style.

Documentation

  • Update README.md if necessary.
  • Keep code documentation up-to-date.

Thank you for your contribution to KistMat AI!