[2605.06716] From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
[Submitted on 7 May 2026]
Title:From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
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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.
Comments: Accepted by ACL 2026 Findings
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2605.06716 [cs.AI]
(or arXiv:2605.06716v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.06716
arXiv-issued DOI via DataCite
Submission history
From: Lin Hongzhan [view email] [v1] Thu, 7 May 2026 03:38:48 UTC (1,557 KB)
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