@inproceedings{wang-etal-2026-colorbrowseragent,
title = "{C}olor{B}rowser{A}gent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution",
author = "Wang, Jihong and
Zhou, Jiamu and
Zhang, Weiming and
Wang, Teng and
Liu, Weiwen and
Zhang, Zhuosheng and
Lou, Xingyu and
Zhang, Weinan and
Deng, Huarong and
Wang, Jun",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.46/",
pages = "665--680",
ISBN = "979-8-89176-394-4",
abstract = "With the advancement of vision-language models, web automation has made significant progress. However, deploying autonomous agents in real-world settings remains challenging, primarily due to site heterogeneity, where generalist models lack domain-specific priors for diverse interfaces, and long-horizon instability, characterized by the accumulation of decision drift over extended interactions. To address these challenges, we introduce ColorBrowserAgent (Complex Long-Horizon Browser Agent), a knowledge-evolving agent for robust web automation. Our approach addresses these challenges through two synergistic mechanisms: human-in-the-loop knowledge adaptation that transforms sparse human feedback into reusable domain knowledge, and knowledge-aligned progressive summarization that stabilizes long interactions through memory compression. Extensive experiments on WebArena, WebChoreArena and industrial deployment show that ColorBrowserAgent consistently outperforms strong baselines. It achieves a state-of-the-art success rate of 71.2{\%} on WebArena and maintains 47.4{\%} performance under zero-shot transfer setting on WebChoreArena. In commercial deployment, it improves user satisfaction by 19.3{\%} relatively, verifying its robustness in real-world scenarios."
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<abstract>With the advancement of vision-language models, web automation has made significant progress. However, deploying autonomous agents in real-world settings remains challenging, primarily due to site heterogeneity, where generalist models lack domain-specific priors for diverse interfaces, and long-horizon instability, characterized by the accumulation of decision drift over extended interactions. To address these challenges, we introduce ColorBrowserAgent (Complex Long-Horizon Browser Agent), a knowledge-evolving agent for robust web automation. Our approach addresses these challenges through two synergistic mechanisms: human-in-the-loop knowledge adaptation that transforms sparse human feedback into reusable domain knowledge, and knowledge-aligned progressive summarization that stabilizes long interactions through memory compression. Extensive experiments on WebArena, WebChoreArena and industrial deployment show that ColorBrowserAgent consistently outperforms strong baselines. It achieves a state-of-the-art success rate of 71.2% on WebArena and maintains 47.4% performance under zero-shot transfer setting on WebChoreArena. In commercial deployment, it improves user satisfaction by 19.3% relatively, verifying its robustness in real-world scenarios.</abstract>
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%0 Conference Proceedings
%T ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution
%A Wang, Jihong
%A Zhou, Jiamu
%A Zhang, Weiming
%A Wang, Teng
%A Liu, Weiwen
%A Zhang, Zhuosheng
%A Lou, Xingyu
%A Zhang, Weinan
%A Deng, Huarong
%A Wang, Jun
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F wang-etal-2026-colorbrowseragent
%X With the advancement of vision-language models, web automation has made significant progress. However, deploying autonomous agents in real-world settings remains challenging, primarily due to site heterogeneity, where generalist models lack domain-specific priors for diverse interfaces, and long-horizon instability, characterized by the accumulation of decision drift over extended interactions. To address these challenges, we introduce ColorBrowserAgent (Complex Long-Horizon Browser Agent), a knowledge-evolving agent for robust web automation. Our approach addresses these challenges through two synergistic mechanisms: human-in-the-loop knowledge adaptation that transforms sparse human feedback into reusable domain knowledge, and knowledge-aligned progressive summarization that stabilizes long interactions through memory compression. Extensive experiments on WebArena, WebChoreArena and industrial deployment show that ColorBrowserAgent consistently outperforms strong baselines. It achieves a state-of-the-art success rate of 71.2% on WebArena and maintains 47.4% performance under zero-shot transfer setting on WebChoreArena. In commercial deployment, it improves user satisfaction by 19.3% relatively, verifying its robustness in real-world scenarios.
%U https://aclanthology.org/2026.acl-industry.46/
%P 665-680
Markdown (Informal)
[ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution](https://aclanthology.org/2026.acl-industry.46/) (Wang et al., ACL 2026)
ACL
- Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang, Huarong Deng, and Jun Wang. 2026. ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 665–680, San Diego, California, USA. Association for Computational Linguistics.