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From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

Paper · 2026 · Jinghao Luo, Yuchen Tian, Chuxue Cao et al.

Summary

LLM-based agents are stateless by default. They struggle to stay coherent across long multi-step tasks and cannot learn from earlier interactions. Work on agent memory is split between an engineering camp that borrows operating-system ideas and a camp that imitates human cognition. Earlier surveys mostly catalogue modules rather than explain how the field has developed.

This survey proposes an evolutionary framework with three formally defined stages:

  • Storage keeps interaction trajectories almost verbatim.
  • Reflection evaluates and corrects single trajectories and writes distilled insights back into the memory repository.
  • Experience induces compact, reusable rules from clusters of similar trajectories, motivated by the Minimum Description Length principle.

The paper follows a why/how/what structure. It names three forces behind this progression: long-horizon consistency, dynamic environments, and continual learning. It also examines active exploration and cross-trajectory abstraction as the defining Experience-stage mechanisms.

The analysis is qualitative, without new experiments. Its value is a shared vocabulary and a roadmap for designing next-generation agent memory, including open gaps such as benchmarks for Experience-stage generalization.

Key points

  • The agent is formalized as a policy that conditions on the system instruction, the current observation, and memory retrieved from a global repository M. Memory is treated as an external store linking frozen parameters to a changing environment.
  • The stages overlap rather than replace one another. A method may keep traits of an earlier stage while essentially belonging to a later one.
  • Proposed future directions are:
    • task-dependent memory triggering,
    • working-memory construction,
    • benchmarks for the Experience stage,
    • distributed shared (consensus) memory for multi-agent systems,
    • multimodal memory.
  • The authors acknowledge limitations: no quantitative comparison, because no unified cross-stage benchmark exists, and a recency bias toward 2024–2025 work, with the Experience stage only emerging in late 2025.
  • A continuously updated paper list is maintained at GitHub (FeishuLuo/Evolving-LLM-Agent-Memory-Survey). A dataset overview is given in Appendix D.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓National University of SingaporeSingapore · Singapore180 works · 34,304 citations🎓Hong Kong University of Science and TechnologyHong Kong · China142 works · 25,136 citations🎓Hong Kong Baptist UniversityHong Kong · China7 works · 246 citations🎓University of Science and Technology BeijingBeijing · China8 works · 315 citations🎓South China Normal UniversityGuangzhou · China5 works · 24 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as 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. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.2069

@inproceedings{luo2026storage,
  title = {{From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms}},
  author = {Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang and Jing Ma},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.2069},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.2069}
}

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