@inproceedings{tang-etal-2026-llm-agent,
title = "Llm agent memory: a survey from a unified representation{--}management perspective",
author = "Tang, Zhenheng and
He, Xin and
Zhao, Tiancheng and
Wei, Fanjunduo and
Liu, Xiang and
Dong, Peijie and
Wang, Qian and
Li, Qi and
Wang, Huacan and
Chen, Ronghao and
Hu, Sen and
Guo, Weidong and
Xu, Yu and
Chen, Haolan and
Lai, Kunfeng and
Zhao, Kaiyong and
Ding, Keyan and
Tsang, Ivor and
Ong, Yew-Soon and
Li, Bo and
Chu, Xiaowen",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.169/",
pages = "3750--3785",
ISBN = "979-8-89176-439-2",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Llm agent memory: a survey from a unified representation–management perspective
%A Tang, Zhenheng
%A He, Xin
%A Zhao, Tiancheng
%A Wei, Fanjunduo
%A Liu, Xiang
%A Dong, Peijie
%A Wang, Qian
%A Li, Qi
%A Wang, Huacan
%A Chen, Ronghao
%A Hu, Sen
%A Guo, Weidong
%A Xu, Yu
%A Chen, Haolan
%A Lai, Kunfeng
%A Zhao, Kaiyong
%A Ding, Keyan
%A Tsang, Ivor
%A Ong, Yew-Soon
%A Li, Bo
%A Chu, Xiaowen
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F tang-etal-2026-llm-agent
%X 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.
%U https://aclanthology.org/2026.aacl-long.169/
%P 3750-3785
Markdown (Informal)
[Llm agent memory: a survey from a unified representation–management perspective](https://aclanthology.org/2026.aacl-long.169/) (Tang et al., AACL-IJCNLP 2026)
ACL
- 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 Tsang, Yew-Soon Ong, Bo Li, and Xiaowen Chu. 2026. Llm agent memory: a survey from a unified representation–management perspective. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3750–3785, Hengqin, China. Association for Computational Linguistics.