MemOS: A Memory OS for AI System
Zhiyu Li, Shichao Song, Chenyang Xi, Hanyu Wang, Chen Tang, Simin Niu, Ding Chen, Jiawei Yang, Chunyu Li, Qingchen Yu, Jihao Zhao, Yezhaohui Wang, Peng Liu, Zehao Lin, Pengyuan Wang, Jiahao Huo, Tianyi Chen, Kai Chen, Kehang Li, Zhen Tao, Junpeng Ren, Huayi Lai
2025-07-08
Summary
This paper talks about MemOS, a memory operating system designed for large language models to better manage different types of memory like text, activations, and model parameters. It treats memory as a system resource that can be stored, retrieved, and updated efficiently to help AI models learn continuously and work smarter.
What's the problem?
The problem is that current AI systems have trouble handling and organizing the huge amounts of information they need to remember, update, and use. This makes it hard for them to learn over time or handle complex, multi-task interactions effectively.
What's the solution?
The researchers created MemOS, which unifies all the different memory types under one system using a concept called MemCube, a standardized container for memory units. MemOS manages memory with a layered architecture that controls storage, scheduling, and access so that AI models can track, update, and reuse memory efficiently while maintaining security and history.
Why it matters?
This matters because by improving how AI manages memory, MemOS helps develop more intelligent and adaptable AI systems that can remember and learn across long periods, making AI better at reasoning, personalization, and multitasking in real-world applications.
Abstract
MemOS, a memory operating system for Large Language Models, addresses memory management challenges by unifying plaintext, activation-based, and parameter-level memories, enabling efficient storage, retrieval, and continual learning.