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.
@article{arxiv.2601.07262,
title = {ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution},
author = {Jihong Wang and Jiamu Zhou and Weiming Zhang and Teng Wang and Weiwen Liu and Zhuosheng Zhang and Xingyu Lou and Weinan Zhang and Huarong Deng and Jun Wang},
journal= {arXiv preprint arXiv:2601.07262},
year = {2026}
}