@inproceedings{li-etal-2026-nested,
    title = "Nested Browser-Use Learning for Agentic Information Seeking",
    author = "Li, Baixuan  and
      Wu, Jialong  and
      Yin, Wenbiao  and
      Li, Kuan  and
      Zhang, Zhongwang  and
      Yin, Huifeng  and
      Tao, Zhengwei  and
      Zhang, Liwen  and
      Xie, Pengjun  and
      Zhou, Jingren  and
      Jiang, Yong  and
      Zhang, Wentao  and
      Gao, Zhiqiang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1049/",
    pages = "22911--22923",
    ISBN = "979-8-89176-390-6",
    abstract = "Information-seeking (IS) agents have achieved strong performance across a range of wide and deep search tasks, yet their tool use remains largely restricted to API-level snippet retrieval and URL-based page fetching, limiting access to the richer information available through real browsing. While full browser interaction could unlock deeper capabilities, its fine-grained control and verbose page content returns introduce substantial complexity for ReAct-style function-calling agents. To bridge this gap, we propose Nested Browser-Use Learning (NestBrowse), which introduces a minimal and complete browser-action framework that decouples interaction control from page exploration through a nested structure. This design simplifies agentic reasoning while enabling effective deep-web information acquisition. Empirical results on challenging deep IS benchmarks demonstrate that NestBrowse offers clear benefits in practice. Further in-depth analyses underscore its efficiency."
}