Implement hybrid TRM-ERS-PMLL model with documentation and fixes.

[drqsatoshi]

This pull request introduces a major extension to the project by adding a hybrid Tiny Recursive Model (TRM) that incorporates advanced memory management and topic-aware reasoning. It also adds a complete agent framework for ARC-AGI-3 game integration, new documentation, and security analysis. The changes maintain backward compatibility and parameter efficiency while greatly enhancing the model's capabilities and usability.

Hybrid TRM Model and Memory Management:

• Implemented a new hybrid TRM model (trm_ers_pmll.py) integrating Enhanced Reconsideration System (ERS), Persistent Memory Logic Loops (PMLL), and a Topic Integrator for structured, persistent, and topic-aware reasoning. This includes features like persistent memory blocks, temporal decay, consensus strengthening, contradiction detection, and knowledge graph integration. Configuration and documentation files were also added for this model.
• Added JSON-based memory persistence with deterministic SHA-256 hashing for reproducible state, and ensured type safety and backward compatibility.

ARC-AGI-3 Agent Framework:

• Introduced a new agent framework for ARC-AGI-3 games, including a base Agent interface (agent.py), agent registry (__init__.py), and core data structures (structs.py). Added a TRM-based agent and support for agent selection. [1] [2] [3]
• Added an MCP tool server (mcp_tool.py) for runtime interaction with the model's persistent memory via standard MCP-compatible clients.

Documentation and Security:

• Added comprehensive documentation: implementation summary, hybrid model usage, benchmarking guide, and ARC-AGI-3 integration instructions. Updated the main README.md to introduce new features and provide usage examples. [1] [2] [3]
• Added a security summary documenting a successful CodeQL scan (no vulnerabilities), code review findings, and best practices followed.

Other Notable Changes:

• Added a .gitignore for build artifacts and a BibTeX citation for the new model. [1] [2]

These changes collectively provide a robust foundation for advanced recursive reasoning, persistent memory, and agent-based benchmarking in ARC-AGI-3 environments.

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Hybrid TRM Model Enhancements:

• Implemented trm_ers_pmll.py with ERS, PMLL, and Topic Integrator, plus configuration and documentation for the hybrid model.
• Added JSON-based memory persistence with deterministic hashing and type safety.

ARC-AGI-3 Agent Integration:

• Introduced agent base class, registry, and data structures for ARC-AGI-3 games, including a TRM agent. [1] [2] [3]
• Added MCP tool server for runtime memory interaction.

Documentation and Security:

• Added detailed documentation, benchmarking, and integration guides; updated README.md. [1] [2] [3]
• Added security summary with CodeQL scan results and code review fixes.

Other:

• Added .gitignore and BibTeX citation for the new model. [1] [2]

[drqsatoshi]

@copilot go over the MCP tools and pop, peek Q promise functions

[drqsatoshi]

Still deleting files not relevant to this project.

However, notebook 55e showed how powerful TRM can be for RNA dataset analysis.

KAGGLE work, however, is being expunged as it’s not related to this project, including arc-agi

[drqsatoshi]

The files that are viewed are what should be merged. The unviewed ones are getting purged.

[drqsatoshi]

Irrelevant schizophrenic files deleted