系统梳理大模型搜索代理的架构与挑战，助力深度智能检索研究。
A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
- 从架构、优化、评估等维度系统分类现有搜索代理方法
- 总结典型应用如Deep Research在复杂任务中的表现能力
- 指出自主规划、评估标准等关键难题，适合研究者参考
大语言模型（LLMs）的出现显著革新了网络搜索。基于LLM的搜索代理标志着向更深层次、动态化和自主信息获取的重要转变。这些代理能够理解用户意图与环境上下文，实现多轮检索与动态规划，将搜索能力拓展至网页之外。以OpenAI的Deep Research为代表的应用展示了其在深度信息挖掘与实际场景中的潜力。本文首次对搜索代理进行系统性分析，从架构、优化、应用与评估角度全面梳理现有工作，识别出关键开放挑战，并提出未来有前景的研究方向。相关资源已开源：https://github.com/YunjiaXi/Awesome-Search-Agent-Papers。
原文摘要 · Abstract (English)
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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