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

arXiv: https://arxiv.org/abs/2512.20651 · HTML: https://mlsanity.com/paper/2512.20651/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025)
- [HaluMem: Evaluating Hallucinations in Memory Systems of Agents](https://mlsanity.com/paper/2511.03506/) (2025)
- [A Scenario-Driven Cognitive Approach to Next-Generation AI Memory](https://mlsanity.com/paper/2509.13235/) (2025)

## Built on by (most cited first, 2 of 2)

- [A-MBER: Affective Memory Benchmark for Emotion Recognition](https://mlsanity.com/paper/2604.07017/) (2026)
- [Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence: A Technical Report](https://mlsanity.com/paper/2603.22306/) (2026)

## Abstract (arXiv)

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".