@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://aclanthology.org/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."
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%0 Conference Proceedings
%T A Survey of Large Language Model-Based Search Agents
%A Xi, Yunjia
%A Lin, Jianghao
%A Xiao, Yongzhao
%A Zhou, Zheli
%A Shan, Rong
%A Gao, Te
%A Zhu, Jiachen
%A Liu, Weiwen
%A Yu, Yong
%A Zhang, Weinan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F xi-etal-2026-survey
%X 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.
%U https://aclanthology.org/2026.acl-long.374/
%P 8244-8279
Markdown (Informal)
[A Survey of Large Language Model-Based Search Agents](https://aclanthology.org/2026.acl-long.374/) (Xi et al., ACL 2026)
ACL
- Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, and Weinan Zhang. 2026. A Survey of Large Language Model-Based Search Agents. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8244–8279, San Diego, California, United States. Association for Computational Linguistics.