@inproceedings{xi-etal-2026-survey,
    title = "A Survey of Large Language Model-Based Search Agents",
    author = "Xi, Yunjia  and
      Lin, Jianghao  and
      Xiao, Yongzhao  and
      Zhou, Zheli  and
      Shan, Rong  and
      Gao, Te  and
      Zhu, Jiachen  and
      Liu, Weiwen  and
      Yu, Yong  and
      Zhang, Weinan",
    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.374/",
    pages = "8244--8279",
    ISBN = "979-8-89176-390-6",
    abstract = "The advent of Large Language Models (LLMs) has significantly revolutionized web search. The emergence of LLM-based Search Agents marks a pivotal shift towards deeper, dynamic, autonomous information seeking. These agents can comprehend user intentions and environment context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web. Leading examples like OpenAI{'}s Deep Research highlight their potential for deep information mining and real-world applications. This survey provides the first systematic analysis of search agents. We comprehensively analyze and categorize existing works from the perspectives of architecture, optimization, application, and evaluation, ultimately identifying critical open challenges and outlining promising future research directions in this rapidly evolving field."
}