MindMemOS: A Portable and Self-Evolving Memory Operating Layer for AI Agents
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Abstract (arXiv)
Memory is a core component of AI agents, enabling them to accumulate experience, maintain personalization, and adapt over long-term interactions. However, existing memory systems often remain fixed after development, limiting their ability to adapt their memory models, organization strategies, and procedural knowledge through continued use. We present MindMemOS, a portable and self-evolving memory operating layer that organizes open-world information using a unified entity property timestructure. MindMemOS supports scenario-adaptive memory modeling, higher-order pattern discovery, autonomous memory refinement, and continuous skill evolution. Its MindMemEvolve algorithm employs validation-driven evolutionary search to optimize memory schemas for target scenarios, whiledreaming consolidates accumulated memories by merging redundant records and resolving conflicts. In addition, implicit corrective feedback serves as a human-in-the-loop signal for identifying and revising potentially inaccurate or misaligned memories. Its MindSkillEvolve algorithm further transforms agent execution trajectories into reusable and progressively refined skills. MindMemOS achieves 94.03% accuracy on LOCOMO and 70.63% on PersonaMem. MindSkillEvolve improves SpreadsheetBench success by 9.2 percentage points over the initial-skill baseline.
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References · 31
- 2023ExpeL: LLM Agents Are Experiential Learnerscited by 814
- 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 800
- 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 722
- 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 622
- 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memorycited by 372
- 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactionscited by 236
- 2025From RAG to Memory: Non-Parametric Continual Learning for Large Language Modelscited by 223
- 2025Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scalecited by 163
- 2025MIRIX: Multi-Agent Memory System for LLM-Based Agentscited by 160
- 2025MemOS: A Memory OS for AI Systemcited by 129
- 2026MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agentscited by 129
- 2024MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentationcited by 118
19 more
- 2026MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memorycited by 113
- 2025G-Memory: Tracing Hierarchical Memory for Multi-Agent Systemscited by 112
- 2024SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulationcited by 100
- 2025MemGen: Weaving Generative Latent Memory for Self-Evolving Agentscited by 99
- 2024MEMORYLLM: Towards Self-Updatable Large Language Modelscited by 89
- 2025MemEvolve: Meta-Evolution of Agent Memory Systemscited by 81
- 2025Nested Learning: The Illusion of Deep Learning Architecturescited by 69
- 2026EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoningcited by 52
- 2026SkillGrad: Optimizing Agent Skills Like Gradient Descentcited by 23
- 2025SCOPE: Prompt Evolution for Enhancing Agent Effectivenesscited by 16
- 2026Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agentscited by 16
- 2026LatentMem: Customizing Latent Memory for Multi-Agent Systemscited by 12
- 2026Fast-weight Product Key Memorycited by 11
- 2026Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memorycited by 5
- 2026Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agentscited by 4
- 2026G-MemLLM: Gated Latent Memory Augmentation for Long-Context Reasoning in Large Language Modelscited by 3
- 2026VikingMem: A Memory Base Management System for Stateful LLM-based Applicationscited by 2
- 2026Retain or Consolidate? Budget-Dependent Operator Selection for Language Agent Memorycited by 2
- 2026ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systemscited by 1
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