@inproceedings{wang-etal-2026-serm,
    title = "{SERM}: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams",
    author = "Wang, Chenglong  and
      Li, Canjia  and
      Zhu, Xingzhao  and
      Huo, Yifu  and
      Wang, Huiyu  and
      Lin, Weixiong  and
      Yang, Yun  and
      He, Qiaozhi  and
      Zhou, Tian Hua  and
      Changxiaojia  and
      Zhu, JingBo  and
      Xiao, Tong",
    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.823/",
    pages = "16687--16706",
    ISBN = "979-8-89176-395-1",
    abstract = "Due to the dynamically evolving nature of real-world query streams, relevance models struggle to generalize to practical search scenarios. A sophisticated solution is self-evolution techniques. However, in large-scale industrial settings with massive query streams, this technique faces two challenges: (1) informative samples are often sparse and difficult to identify, and (2) pseudo-labels generated by the current model could be unreliable. To address these challenges, in this work, we propose a Self-Evolving Relevance Model approach (SERM), which comprises two complementary multi-agent modules: a multi-agent sample miner, designed to detect distributional shifts and identify informative training samples, and a multi-agent relevance annotator, which provides reliable labels through a two-level agreement framework. We evaluated SERM on a large-scale industrial platform, which serves billions of user requests daily. Experimental results demonstrate that SERM can achieve significant performance gains through iterative self-evolution, as validated by extensive offline multilingual evaluations and online testing."
}