MemOS: A Memory OS for AI System

2025-07-04

AI Summary

Large language models (LLMs) lack a unified memory management framework, hindering long-context reasoning, continual personalization, and knowledge consistency. Existing approaches—relying on static parameters, transient context windows, or stateless retrieval-augmented generation (RAG)—fail to support heterogeneous knowledge co-evolution across temporal scales and sources. Method: We propose MemOS, a memory operating system for LLMs, introducing the MemCube as a foundational memory abstraction that unifies explicit, activation-level, and parameter-level memory representations, enabling composition, migration, and fusion. MemOS employs hierarchical modeling integrating retrieval, activation control, and parameter updates to realize a schedulable, evolvable, lifecycle-aware memory management architecture. Results: Experiments demonstrate that MemOS significantly reduces training and inference overhead, improves knowledge update efficiency and context management capability, and provides a scalable, controllable memory infrastructure for continual learning and personalized modeling.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the development of long-context reasoning, continual personalization, and knowledge consistency.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.While Retrieval-Augmented Generation (RAG) introduces external knowledge in plain text, it remains a stateless workaround without lifecycle control or integration with persistent representations.Recent work has modeled the training and inference cost of LLMs from a memory hierarchy perspective, showing that introducing an explicit memory layer between parameter memory and external retrieval can substantially reduce these costs by externalizing specific knowledge. Beyond computational efficiency, LLMs face broader challenges arising from how information is distributed over time and context, requiring systems capable of managing heterogeneous knowledge spanning different temporal scales and sources. To address this challenge, we propose MemOS, a memory operating system that treats memory as a manageable system resource. It unifies the representation, scheduling, and evolution of plaintext, activation-based, and parameter-level memories, enabling cost-efficient storage and retrieval. As the basic unit, a MemCube encapsulates both memory content and metadata such as provenance and versioning. MemCubes can be composed, migrated, and fused over time, enabling flexible transitions between memory types and bridging retrieval with parameter-based learning. MemOS establishes a memory-centric system framework that brings controllability, plasticity, and evolvability to LLMs, laying the foundation for continual learning and personalized modeling.

Problem

• Lack of memory management in LLMs hinders long-context reasoning.
• Static parameters limit tracking user preferences over time.
• Current systems fail to manage heterogeneous knowledge efficiently.

Innovation

• Introduces MemOS as a memory operating system
• Unifies representation and scheduling of memory types
• Uses MemCubes for flexible memory management