MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models

• URL: http://arxiv.org/abs/2505.22101v1
• Date: Wed, 28 May 2025 08:27:12 GMT
• Title: MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
• Authors: Zhiyu Li, Shichao Song, Hanyu Wang, Simin Niu, Ding Chen, Jiawei Yang, Chenyang Xi, Huayi Lai, Jihao Zhao, Yezhaohui Wang, Junpeng Ren, Zehao Lin, Jiahao Huo, Tianyi Chen, Kai Chen, Kehang Li, Zhiqiang Yin, Qingchen Yu, Bo Tang, Hongkang Yang, Zhi-Qin John Xu, Feiyu Xiong,
• Abstract summary: We introduce MemOS, a memory operating system designed for Large Language Models (LLMs)<n>At its core is the MemCube, a standardized memory abstraction that enables tracking, fusion, and migration of heterogeneous memory.<n>MemOS establishes a memory-centric execution framework with strong controllability, adaptability, and evolvability.
• Score: 31.944531660401722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language perception and generation, current LLMs fundamentally lack a unified and structured architecture for handling memory. They primarily rely on parametric memory (knowledge encoded in model weights) and ephemeral activation memory (context-limited runtime states). While emerging methods like Retrieval-Augmented Generation (RAG) incorporate plaintext memory, they lack lifecycle management and multi-modal integration, limiting their capacity for long-term knowledge evolution. To address this, we introduce MemOS, a memory operating system designed for LLMs that, for the first time, elevates memory to a first-class operational resource. It builds unified mechanisms for representation, organization, and governance across three core memory types: parametric, activation, and plaintext. At its core is the MemCube, a standardized memory abstraction that enables tracking, fusion, and migration of heterogeneous memory, while offering structured, traceable access across tasks and contexts. MemOS establishes a memory-centric execution framework with strong controllability, adaptability, and evolvability. It fills a critical gap in current LLM infrastructure and lays the groundwork for continual adaptation, personalized intelligence, and cross-platform coordination in next-generation intelligent systems.

Related papers

• MemOS: A Memory OS for AI System [116.87568350346537]
  Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI)<n>Existing models mainly rely on static parameters and short-lived contextual states, limiting their ability to track user preferences or update knowledge over extended periods.<n>MemOS is a memory operating system that treats memory as a manageable system resource.
  arXiv  Detail & Related papers  (2025-07-04T17:21:46Z)
• Memory OS of AI Agent [3.8665965906369375]
  Large Language Models (LLMs) face a crucial challenge from fixed context windows and inadequate memory management.<n>We propose a Memory Operating System, i.e., MemoryOS, to achieve comprehensive and efficient memory management for AI agents.
  arXiv  Detail & Related papers  (2025-05-30T15:36:51Z)
• Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions [55.19217798774033]
  Memory is a fundamental component of AI systems, underpinning large language models (LLMs)-based agents.<n>In this survey, we first categorize memory representations into parametric and contextual forms.<n>We then introduce six fundamental memory operations: Consolidation, Updating, Indexing, Forgetting, Retrieval, and Compression.
  arXiv  Detail & Related papers  (2025-05-01T17:31:33Z)
• A-MEM: Agentic Memory for LLM Agents [42.50876509391843]
  Large language model (LLM) agents require memory systems to leverage historical experiences.<n>Current memory systems enable basic storage and retrieval but lack sophisticated memory organization.<n>This paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way.
  arXiv  Detail & Related papers  (2025-02-17T18:36:14Z)
• MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training [24.066283519769968]
  Large Language Models (LLMs) have been trained using extended context lengths to foster more creative applications.<n>We propose MEMO, a novel framework for fine-grained activation memory management.<n>MeMO achieves an average of 1.97x and 1.80x MFU compared to Megatron-LM and DeepSpeed.
  arXiv  Detail & Related papers  (2024-07-16T18:59:49Z)
• B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory [91.81390121042192]
  We develop a class of models called B'MOJO to seamlessly combine eidetic and fading memory within an composable module. B'MOJO's ability to modulate eidetic and fading memory results in better inference on longer sequences tested up to 32K tokens.
  arXiv  Detail & Related papers  (2024-07-08T18:41:01Z)
• MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory [49.96019697955383]
  We introduce MemLLM, a novel method of enhancing large language models (LLMs) by integrating a structured and explicit read-and-write memory module.<n>Our experiments indicate that MemLLM enhances the LLM's performance and interpretability, in language modeling in general and knowledge-intensive tasks in particular.
  arXiv  Detail & Related papers  (2024-04-17T18:13:16Z)
• Online Adaptation of Language Models with a Memory of Amortized Contexts [82.02369596879817]
  Memory of Amortized Contexts (MAC) is an efficient and effective online adaptation framework for large language models. We show how MAC can be combined with and improve the performance of popular alternatives such as retrieval augmented generations.
  arXiv  Detail & Related papers  (2024-03-07T08:34:57Z)
• Empowering Working Memory for Large Language Model Agents [9.83467478231344]
  This paper explores the potential of applying cognitive psychology's working memory frameworks to large language models (LLMs) An innovative model is proposed incorporating a centralized Working Memory Hub and Episodic Buffer access to retain memories across episodes. This architecture aims to provide greater continuity for nuanced contextual reasoning during intricate tasks and collaborative scenarios.
  arXiv  Detail & Related papers  (2023-12-22T05:59:00Z)
• MemGPT: Towards LLMs as Operating Systems [50.02623936965231]
  Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows. We propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems. We release MemGPT code and data for our experiments at https://memgpt.ai.
  arXiv  Detail & Related papers  (2023-10-12T17:51:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.