@inproceedings{xiong-etal-2026-memory,
    title = "How Memory Management Impacts {LLM} Agents: An Empirical Study of Experience-Following Behavior",
    author = "Xiong, Zidi  and
      Lin, Yuping  and
      Xie, Wenya  and
      He, Pengfei  and
      Liu, Zirui  and
      Tang, Jiliang  and
      Lakkaraju, Himabindu  and
      Xiang, Zhen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.27/",
    pages = "623--645",
    ISBN = "979-8-89176-390-6",
    abstract = "Memory is a critical component in large language model (LLM)-based agents, enabling them to store and retrieve past executions to improve task performance over time. In this paper, we conduct an empirical study on how memory management choices impact the LLM agents' behavior, especially their long-term performance. Specifically, we focus on two fundamental memory management operations that are widely used by many agent frameworks{---}memory addition and deletion{---}to systematically study their impact on the agent behavior. Through our quantitative analysis, we find that LLM agents display an *experience-following* property: high similarity between a task input and the input in a retrieved memory record often results in highly similar agent outputs. Our analysis further reveals two significant challenges associated with this property: *error propagation*, where inaccuracies in past experiences compound and degrade future performance, and *misaligned experience replay*, where some seemingly correct executions can provide limited or even misleading value as experiences. Through controlled experiments, we demonstrate the importance of regulating experience quality within the memory bank and show that future task evaluations can serve as free quality labels for stored memory. Our findings offer insights into the behavioral dynamics of LLM agent memory systems and provide practical guidance for designing memory components that support robust, long-term agent performance."
}