@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T ExpSeek: Self-Triggered Experience Seeking for Web Agents
%A Zhang, Wenyuan
%A Zhang, Xinghua
%A Yu, Haiyang
%A Nie, Shuaiyi
%A Wu, Bingli
%A Yue, Juwei
%A Liu, Tingwen
%A Li, Yongbin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%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 zhang-etal-2026-expseek
%X 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.
%U https://aclanthology.org/2026.findings-acl.1462/
%P 29255-29283
Markdown (Informal)
[ExpSeek: Self-Triggered Experience Seeking for Web Agents](https://aclanthology.org/2026.findings-acl.1462/) (Zhang et al., Findings 2026)
ACL
- Wenyuan Zhang, Xinghua Zhang, Haiyang Yu, Shuaiyi Nie, Bingli Wu, Juwei Yue, Tingwen Liu, and Yongbin Li. 2026. ExpSeek: Self-Triggered Experience Seeking for Web Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 29255–29283, San Diego, California, United States. Association for Computational Linguistics.