@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 (Volume 6: Industry Track)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.46/",
doi = "10.18653/v1/2026.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 (Volume 6: Industry Track)
%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.
%R 10.18653/v1/2026.acl-industry.46
%U https://aclanthology.org/2026.acl-industry.46/
%U https://doi.org/10.18653/v1/2026.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 (Volume 6: Industry Track), pages 665–680, San Diego, California, USA. Association for Computational Linguistics.