A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912543424905216 |
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| author | Xi, Yunjia Lin, Jianghao Xiao, Yongzhao Zhou, Zheli Shan, Rong Gao, Te Zhu, Jiachen Liu, Weiwen Yu, Yong Zhang, Weinan |
| author_facet | Xi, Yunjia Lin, Jianghao Xiao, Yongzhao Zhou, Zheli Shan, Rong Gao, Te Zhu, Jiachen Liu, Weiwen Yu, Yong Zhang, Weinan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05668 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges Xi, Yunjia Lin, Jianghao Xiao, Yongzhao Zhou, Zheli Shan, Rong Gao, Te Zhu, Jiachen Liu, Weiwen Yu, Yong Zhang, Weinan Information Retrieval Artificial Intelligence Computation and Language 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. |
| title | A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges |
| topic | Information Retrieval Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2508.05668 |