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 evaluate SERM in a large-scale industrial setting, 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.
@article{arxiv.2601.09515,
title = {SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams},
author = {Chenglong Wang and Canjia Li and Xingzhao Zhu and Yifu Huo and Huiyu Wang and Weixiong Lin and Yun Yang and Qiaozhi He and Tianhua Zhou and Xiaojia Chang and Jingbo Zhu and Tong Xiao},
journal= {arXiv preprint arXiv:2601.09515},
year = {2026}
}