@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams
%A Wang, Chenglong
%A Li, Canjia
%A Zhu, Xingzhao
%A Huo, Yifu
%A Wang, Huiyu
%A Lin, Weixiong
%A Yang, Yun
%A He, Qiaozhi
%A Zhou, Tian Hua
%A Zhu, JingBo
%A Xiao, Tong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Changxiaojia
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-serm
%X 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.
%U https://aclanthology.org/2026.findings-acl.823/
%P 16687-16706
Markdown (Informal)
[SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams](https://aclanthology.org/2026.findings-acl.823/) (Wang et al., Findings 2026)
ACL
- Chenglong Wang, Canjia Li, Xingzhao Zhu, Yifu Huo, Huiyu Wang, Weixiong Lin, Yun Yang, Qiaozhi He, Tian Hua Zhou, Changxiaojia, JingBo Zhu, and Tong Xiao. 2026. SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams. In Findings of the Association for Computational Linguistics: ACL 2026, pages 16687–16706, San Diego, California, United States. Association for Computational Linguistics.