LLM Agent Memory: A Survey from a Unified Representation–Management Perspective
Rédigé par Zhenheng Tang, Xin He, Tiancheng Zhao, Fanjunduo Wei, Xiang Liu, Peijie Dong, Qian Wang, Qi Li, Huacan Wang, Ronghao Chen, Sen Hu, Weidong Guo, Yu Xu, Haolan Chen, Kunfeng Lai, Kaiyong Zhao, Keyan Ding, Ivor W. Tsang, Yew-Soon Ong, Bo Li et Xiaowen Chu
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Résumé
Large language models (LLMs) face significant challenges in sustaining long-term memory for agentic applications due to limited context windows. To address this limitation, many work has proposed diverse memory mechanisms to support long-term, multi-turn interactions, leveraging different approaches tailored to distinct memory storage objects, such as KV caches. In this survey, we present a unified taxonomy that organizes memory systems for long-context scenarios by decoupling memory abstractions from model-specific inference and training methods. We categorize LLM memory into three primary paradigms: natural language tokens, intermediate representations and parameters. For each paradigm, we organize existing methods by three management stages, including memory construction, update, and query, so that long-context memory mechanisms can be described in a consistent way across system designs, with their implementation choices and constraints made explicit. Finally, we outline key research directions for long-context memory system design.
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