Related papers: A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges

A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges

URL: http://arxiv.org/abs/2508.05668v3
Date: Tue, 19 Aug 2025 05:15:19 GMT
Title: A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
Authors: Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang,
Abstract summary: Large Language Models (LLMs) have revolutionized web search.<n>These agents can comprehend user intentions and environmental context.<n>This survey provides the first systematic analysis of search agents.
Score: 30.146391942071126
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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 environmental 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. Our repository is available on https://github.com/YunjiaXi/Awesome-Search-Agent-Papers.

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