@inproceedings{li-etal-2026-beyond-experience,
    title = "Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen {LLM}s",
    author = "Li, Xuancheng  and
      Li, Haitao  and
      Zhou, Yujia  and
      Liu, Yiqun  and
      Ai, Qingyao",
    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.1831/",
    pages = "39467--39482",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes. Prior experience reuse typically relies on external retrieval, which is similarity-based, can introduce noise, and adds latency. We introduce \textbf{SEAM} (\textbf{S}tructured \textbf{E}xperience \textbf{A}dapter \textbf{M}odule), a lightweight, executor-specific plug-in that stores experience in its parameters and generates a structured, instance-tailored experience entry in a single forward pass to guide a frozen LLM executor. SEAM is trained for utility via executor rollouts and GRPO while keeping the executor frozen, and can be further improved with logged-success SFT after deployment. Experiments on mathematical reasoning benchmarks show consistent accuracy gains across executors with low overhead. Extensive ablation and analysis further elucidate the mechanisms underlying SEAM{'}s effectiveness and robustness.[We release our code at {\ensuremath{<}}https://anonymous.4open.science/r/SEAM{\ensuremath{>}}.]"
}