Enhance Kistmat_AI to Meet Original Vision Requirements

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Summary

Enhance the Kistmat_AI system to fully meet the original vision requirements, including dynamic optimization, curriculum learning, autonomous dataset generation, advanced memory systems, and comprehensive evaluation mechanisms.

Requirements and Proposed Enhancements

1. Automatic Mathematical Problem Solving:

   • Ensure the system can solve problems from basic to advanced levels.
   • Implement additional algorithms for advanced mathematical problem-solving.

2. Theorem Generation and Abstraction:

   • Implement modules for generating and validating mathematical theorems.

3. Dynamic Optimization of Parameters and Architecture:

   • Integrate AutoML techniques for dynamic optimization of hyperparameters and model architecture.

4. Comprehensive Curriculum Learning:

   • Extend curriculum learning to cover all educational levels from preschool to doctoral studies.
   • Ensure dynamic adaptation of the curriculum based on the system's progress.

5. Autonomous Dataset Generation:

   • Expand dataset generation to cover all educational levels.
   • Ensure datasets are generated dynamically based on the system's needs.

6. Advanced Memory Systems:

   • Ensure full integration and optimization of formulative, conceptual, long-term, short-term, and inferential memories.

7. Integration of Liquid Neural Networks, PPO, and Few-Shot Learning:

   • Implement liquid neural networks, PPO, and prototypical networks.
   • Ensure these models are interconnected and used efficiently by the system.

8. Dynamic and Comprehensive Evaluation:

   • Evaluation Metrics:
     • Implement a variety of evaluation metrics, including accuracy, precision, recall, F1-score, mean squared error (MSE), R-squared (R²), and others relevant to mathematical problem-solving.
     • Develop custom metrics to evaluate the system's ability to generate and validate theorems, create formulations, and adapt dynamically.
   • Automated Evaluation Pipeline:
     • Create an automated evaluation pipeline that runs at the end of each training epoch and curriculum stage.
     • Integrate this pipeline with the training loop to provide real-time feedback and adjustments.
   • Dynamic Readiness Evaluation:
     • Implement dynamic readiness evaluation to determine if the model is ready to advance to the next curriculum stage.
     • Use thresholds and criteria that adapt based on the model's performance and learning rate.
   • Cross-Validation and Holdout Sets:
     • Use cross-validation techniques to ensure robust evaluation across different data splits.
     • Maintain holdout sets for final evaluation to prevent overfitting and ensure generalization.
   • Memory Utilization and Efficiency:
     • Evaluate the efficiency and utilization of different memory components (formulative, conceptual, long-term, short-term, inferential).
     • Implement metrics to monitor memory usage, retrieval accuracy, and update efficiency.
   • Performance Monitoring:
     • Monitor the system's performance over time, including training time, inference time, and resource utilization (CPU, GPU, memory).
     • Implement alerts and logs for performance degradation or anomalies.
   • User Feedback Integration:
     • Develop mechanisms to incorporate user feedback into the evaluation process.
     • Allow users to provide feedback on the system's solutions, formulations, and generated theorems.
   • Visualization and Reporting:
     • Create detailed visualizations and reports of the evaluation results.
     • Include charts, graphs, and tables to present metrics, performance trends, and areas for improvement.
   • Continuous Improvement Loop:
     • Implement a continuous improvement loop where evaluation results are used to refine and optimize the model and its components.
     • Use evaluation insights to guide hyperparameter tuning, curriculum adjustments, and memory updates.

9. User Interface for Progress and Results Visualization:

   • Create a user interface to visualize epochs, levels, study grades, problem examples, and results.

Tasks

• Implement advanced mathematical problem-solving algorithms.
• Develop modules for theorem generation and validation.
• Integrate AutoML techniques for dynamic optimization.
• Extend and adapt curriculum learning.
• Expand and optimize autonomous dataset generation.
• Ensure full integration of advanced memory systems.
• Implement and integrate liquid neural networks, PPO, and few-shot learning.
• Develop dynamic and comprehensive evaluation mechanisms:
  • Implement a variety of evaluation metrics.
  • Create an automated evaluation pipeline.
  • Implement dynamic readiness evaluation.
  • Use cross-validation and holdout sets.
  • Evaluate memory utilization and efficiency.
  • Monitor performance over time.
  • Develop mechanisms for user feedback integration.
  • Create detailed visualizations and reports.
  • Implement a continuous improvement loop.
• Create a user interface for progress and results visualization.

Additional Notes

• Ensure all new features are well-documented and tested.
• Maintain modularity and scalability in the codebase to facilitate future enhancements.