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Memory Bear AI A Breakthrough from Memory to Cognition Toward Artificial General Intelligence

Deliang Wen, Ke Sun

TL;DR

Memory Bear addresses the critical memory bottlenecks of large language models by introducing a cognitive-inspired, three-layer memory architecture that unites explicit and implicit memory with an activation-based forgetting mechanism. It combines a Memory Extraction Engine, semantic pruning, self-reflection, and cross-agent coordination to achieve durable, coherent long-term memory and higher-quality reasoning across domains. The approach yields tangible gains in accuracy, token efficiency, and hallucination reduction, while delivering practical benefits in customer service, healthcare, enterprise operations, and education. This work demonstrates a viable path toward AI that can remember, reason, and adapt over long horizons, offering a foundation for scalable memory-cognition systems and advancing the prospect of AGI-informed intelligent agents.

Abstract

Large language models (LLMs) face inherent limitations in memory, including restricted context windows, long-term knowledge forgetting, redundant information accumulation, and hallucination generation. These issues severely constrain sustained dialogue and personalized services. This paper proposes the Memory Bear system, which constructs a human-like memory architecture grounded in cognitive science principles. By integrating multimodal information perception, dynamic memory maintenance, and adaptive cognitive services, Memory Bear achieves a full-chain reconstruction of LLM memory mechanisms. Across domains such as healthcare, enterprise operations, and education, Memory Bear demonstrates substantial engineering innovation and performance breakthroughs. It significantly improves knowledge fidelity and retrieval efficiency in long-term conversations, reduces hallucination rates, and enhances contextual adaptability and reasoning capability through memory-cognition integration. Experimental results show that, compared with existing solutions (e.g., Mem0, MemGPT, Graphiti), Memory Bear outperforms them across key metrics, including accuracy, token efficiency, and response latency. This marks a crucial step forward in advancing AI from "memory" to "cognition".

Memory Bear AI A Breakthrough from Memory to Cognition Toward Artificial General Intelligence

TL;DR

Memory Bear addresses the critical memory bottlenecks of large language models by introducing a cognitive-inspired, three-layer memory architecture that unites explicit and implicit memory with an activation-based forgetting mechanism. It combines a Memory Extraction Engine, semantic pruning, self-reflection, and cross-agent coordination to achieve durable, coherent long-term memory and higher-quality reasoning across domains. The approach yields tangible gains in accuracy, token efficiency, and hallucination reduction, while delivering practical benefits in customer service, healthcare, enterprise operations, and education. This work demonstrates a viable path toward AI that can remember, reason, and adapt over long horizons, offering a foundation for scalable memory-cognition systems and advancing the prospect of AGI-informed intelligent agents.

Abstract

Large language models (LLMs) face inherent limitations in memory, including restricted context windows, long-term knowledge forgetting, redundant information accumulation, and hallucination generation. These issues severely constrain sustained dialogue and personalized services. This paper proposes the Memory Bear system, which constructs a human-like memory architecture grounded in cognitive science principles. By integrating multimodal information perception, dynamic memory maintenance, and adaptive cognitive services, Memory Bear achieves a full-chain reconstruction of LLM memory mechanisms. Across domains such as healthcare, enterprise operations, and education, Memory Bear demonstrates substantial engineering innovation and performance breakthroughs. It significantly improves knowledge fidelity and retrieval efficiency in long-term conversations, reduces hallucination rates, and enhances contextual adaptability and reasoning capability through memory-cognition integration. Experimental results show that, compared with existing solutions (e.g., Mem0, MemGPT, Graphiti), Memory Bear outperforms them across key metrics, including accuracy, token efficiency, and response latency. This marks a crucial step forward in advancing AI from "memory" to "cognition".
Paper Structure (42 sections, 2 equations, 13 figures)

This paper contains 42 sections, 2 equations, 13 figures.

Table of Contents

  1. Problem Background
  2. Theoretical Foundation
  3. System Architecture
  4. Storage Layer
  5. Orchestration Layer
  6. Application Layer
  7. Technical Innovations
  8. Intelligent Semantic Pruning Algorithm
  9. Memory Extraction Engine
  10. Memory Forgetting Engine
  11. Base-Level Activation (BLA).
  12. Memory Activation.
  13. Forgetting Mechanism.
  14. Self-Reflection Engine
  15. Reflection Mechanism.
  16. ...and 27 more sections

Figures (13)

  • Figure 1: Core technologies, application results, and future development directions of Memory Bear.
  • Figure 2.1: HUMAN VS. AI: The MEMORY PARALLEL.
  • Figure 3.1: Memory Bear System Architecture
  • Figure 3.2: Memory Bear Storage Layer Architecture
  • Figure 3.3: Application Layer
  • ...and 8 more figures