@inproceedings{cao-etal-2026-remember,
    title = "Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution",
    author = "Cao, Zouying  and
      Deng, Jiaji  and
      Yu, Li  and
      Zhou, Weikang  and
      Liu, Zhaoyang  and
      Ding, Bolin  and
      Zhao, Hai",
    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://preview.aclanthology.org/ingest-acl/2026.findings-acl.829/",
    pages = "16803--16822",
    ISBN = "979-8-89176-395-1",
    abstract = "Procedural memory enables large language model (LLM) agents to internalize ``how-to'' knowledge and thus reduce redundant trial-and-error. However, existing frameworks predominantly suffer from a ``passive accumulation'' paradigm, treating memory as a static append-only archive. To bridge the gap between static storage and dynamic reasoning, we propose ReMe (Remember Me, Refine Me), a comprehensive framework for experience-driven agent evolution. ReMe manages the memory lifecycle via three mechanisms: 1) multi-faceted distillation, which extracts fine-grained experiences by recognizing success patterns, analyzing failure triggers and generating comparative insights; 2) context-adaptive reuse, which tailors historical insights to new contexts through scenario-aware indexing; and 3) utility-based refinement, which automatically adds validated memories and prunes outdated ones to maintain a compact, high-quality experience pool. Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system. Crucially, we observe a significant memory-scaling effect: Qwen3-8B equipped with ReMe outperforms larger, memoryless Qwen3-14B, indicating that self-evolving memory provides a computation-efficient path for lifelong learning."
}