@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://preview.aclanthology.org/ingest-acl/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."
}