@inproceedings{zhang-etal-2026-expseek,
    title = "{E}xp{S}eek: Self-Triggered Experience Seeking for Web Agents",
    author = "Zhang, Wenyuan  and
      Zhang, Xinghua  and
      Yu, Haiyang  and
      Nie, Shuaiyi  and
      Wu, Bingli  and
      Yue, Juwei  and
      Liu, Tingwen  and
      Li, Yongbin",
    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.1462/",
    pages = "29255--29283",
    ISBN = "979-8-89176-395-1",
    abstract = "Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insights from accumulated experiences. However, existing methods predominantly inject experience passively as global context before task execution, struggling to adapt to dynamically changing contextual observations during agent-environment interaction. We propose **ExpSeek**, which shifts experience toward step-level proactive seeking: (1) estimating step-level entropy thresholds to determine intervention timing using the model{'}s intrinsic signals; (2) designing step-level tailored experience content. Experiments on Qwen3-8B and 32B models across four challenging web agent benchmarks demonstrate that ExpSeek achieves absolute improvements of 9.3{\%} and 7.5{\%}, respectively. Our experiments validate the feasibility and advantages of entropy as a self-triggering signal, reveal that even a small-scale 4B experience model can significantly boost the performance of larger agent models. The code is released at https://github.com/WYRipple/ExpSeek."
}