MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
AI Summary
China's answer to MemGPT, from Shanghai Jiao Tong and collaborators, May 2025. MemOS argues that LLMs currently juggle three incompatible memory types — parametric (weights), activation (runtime state), and plaintext (RAG) — with no unified interface. Their proposal is MemCube, a standardized container that can hold any of the three and supports tracking, fusion, and migration between them. Ambitious in scope and early in implementation, but it's the most aggressive attempt so far at treating memory as a first-class system resource rather than a retrieval problem.
Original excerpt
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: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…
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