@inproceedings{luo-etal-2026-storage,
title = "From Storage to Experience: A Survey on the Evolution of {LLM} Agent Memory Mechanisms",
author = "Luo, Jinghao and
Tian, Yuchen and
Cao, Chuxue and
Luo, Ziyang and
Lin, Hongzhan and
Li, Kaixin and
Kong, Chuyi and
Yang, Ruichao and
Ma, Jing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2069/",
pages = "41622--41652",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have emerged as the architectural cornerstone of these systems, current research remains fragmented, oscillating between operating system engineering and cognitive science. This theoretical divide prevents a unified view of technological synthesis and a coherent evolutionary perspective. To bridge this gap, this survey proposes a novel evolutionary framework for LLM agent memory mechanisms, formalizing the development process into three stages: **Storage** (trajectory preservation), **Reflection** (trajectory refinement), and **Experience** (trajectory abstraction). We first formally define these three stages before analyzing the three core drivers of this evolution: the necessity for long-range consistency, the challenges in dynamic environments, and the ultimate goal of continual learning. Furthermore, we specifically explore two transformative mechanisms in the frontier Experience stage: proactive exploration and cross-trajectory abstraction. By synthesizing these disparate views, this work offers robust design principles and a clear roadmap for the development of next-generation LLM agents."
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<abstract>Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have emerged as the architectural cornerstone of these systems, current research remains fragmented, oscillating between operating system engineering and cognitive science. This theoretical divide prevents a unified view of technological synthesis and a coherent evolutionary perspective. To bridge this gap, this survey proposes a novel evolutionary framework for LLM agent memory mechanisms, formalizing the development process into three stages: **Storage** (trajectory preservation), **Reflection** (trajectory refinement), and **Experience** (trajectory abstraction). We first formally define these three stages before analyzing the three core drivers of this evolution: the necessity for long-range consistency, the challenges in dynamic environments, and the ultimate goal of continual learning. Furthermore, we specifically explore two transformative mechanisms in the frontier Experience stage: proactive exploration and cross-trajectory abstraction. By synthesizing these disparate views, this work offers robust design principles and a clear roadmap for the development of next-generation LLM agents.</abstract>
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%0 Conference Proceedings
%T From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
%A Luo, Jinghao
%A Tian, Yuchen
%A Cao, Chuxue
%A Luo, Ziyang
%A Lin, Hongzhan
%A Li, Kaixin
%A Kong, Chuyi
%A Yang, Ruichao
%A Ma, Jing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F luo-etal-2026-storage
%X Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have emerged as the architectural cornerstone of these systems, current research remains fragmented, oscillating between operating system engineering and cognitive science. This theoretical divide prevents a unified view of technological synthesis and a coherent evolutionary perspective. To bridge this gap, this survey proposes a novel evolutionary framework for LLM agent memory mechanisms, formalizing the development process into three stages: **Storage** (trajectory preservation), **Reflection** (trajectory refinement), and **Experience** (trajectory abstraction). We first formally define these three stages before analyzing the three core drivers of this evolution: the necessity for long-range consistency, the challenges in dynamic environments, and the ultimate goal of continual learning. Furthermore, we specifically explore two transformative mechanisms in the frontier Experience stage: proactive exploration and cross-trajectory abstraction. By synthesizing these disparate views, this work offers robust design principles and a clear roadmap for the development of next-generation LLM agents.
%U https://aclanthology.org/2026.findings-acl.2069/
%P 41622-41652
Markdown (Informal)
[From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms](https://aclanthology.org/2026.findings-acl.2069/) (Luo et al., Findings 2026)
ACL
- Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang, and Jing Ma. 2026. From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms. In Findings of the Association for Computational Linguistics: ACL 2026, pages 41622–41652, San Diego, California, United States. Association for Computational Linguistics.