Hierarchical Reasoning Model (HRM) - Enhanced Memory Framework

Overview

This project builds upon the original "Hierarchical Reasoning Model (HRM)", designed to simulate step-wise, multi-layered reasoning for AI systems. The HRM operates on structured JSON exchanges between reasoning layers to progressively generate output.

We have enhanced the core HRM system by integrating a persistent memory base and enriching JSON metadata, enabling context-aware reasoning, knowledge accumulation, and efficient information retrieval.

Original Concept Acknowledgement

The foundational HRM architecture, including the layered reasoning loop and JSON-based information flow, was originally conceptualized by
Guan Wang1,†, Jin Li1, Yuhao Sun1, Xing Chen1, Changling Liu1, Yue Wu1, Meng Lu1,†, Sen Song2,†, Yasin Abbasi Yadkori1,† 1Sapient Intelligence, Singapore.
THE ORIGINAL PDF
Their design serves as the backbone for structured, hierarchical AI reasoning.

Key Enhancements in This Implementation

• Scalable Memory Base:

  • Adds a graph or tree-like structured memory base to store diverse knowledge.
  • Segments data by model type, interaction history, and web-acquired knowledge.

• Tag-Based Knowledge Access:

  • Enables layers to retrieve relevant information using tags and keywords.
  • Improves system efficiency by narrowing information lookups.

• Extended JSON Metadata:

  • JSON payloads exchanged between layers carry additional context tags.
  • Metadata includes memory references and reasoning state descriptors.

• Centralized Memory Interface:

  • Abstracts all read/write operations to the memory base.
  • Ensures modular layer interaction without direct memory manipulation.

High-Level System Flow

1. Encoding Phase:

   • Combines system prompts and user input into a structured JSON request.

2. Hierarchical Layer Reasoning:

   • Each layer processes the JSON, extracts tags, queries memory, and augments the payload.

3. Memory Integration:

   • Interface handles memory lookups using extracted tags.

4. Output Generation:

   • Final layer compiles output, decoded and presented to the user.

Benefits of the Enhanced HRM

• Real-time, context-aware knowledge retrieval.
• Persistent memory enables cumulative learning.
• Tag-based lookup optimizes information access.
• Modular, scalable system aligned with human-like reasoning.

Future Development Roadmap

• Optimize memory structure for large-scale deployments.
• Enable knowledge merging across domains.
• Expand system to incorporate real-time external knowledge sources.

License & Credits

This project extends the original Hierarchical Reasoning Model architecture for research and development purposes. All credit for the core system design belongs to the original creator(s), acknowledged here.