深度研究：系统性综述

摘要

大型语言模型（LLMs）已从文本生成工具迅速发展为强大的问题解决系统。然而，许多开放型任务需要批判性思维、多源信息整合和可验证的输出，这已超出单次提示或标准检索增强生成的能力范围。近期，大量研究开始探索深度研究（DR）范式，旨在将LLMs的推理能力与搜索引擎等外部工具相结合，使LLMs能够作为研究代理完成复杂的开放式任务。本文对深度研究系统进行了全面系统的梳理，包括清晰的发展路径、基础组件、实践技术、核心挑战与未来方向。具体而言，我们的主要贡献包括：（i）提出三阶段发展路径框架，明确区分深度研究与其他相关范式；（ii）系统介绍四大核心组件：查询规划、信息获取、记忆管理与答案生成，并为每个组件建立细粒度分类体系；（iii）总结提示工程、监督微调、智能体强化学习等优化技术；（iv）整合评估标准与开放挑战，为未来发展提供指引。随着深度研究领域的快速演进，我们将持续更新本综述以反映该领域的最新进展。

English

Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable outputs, which are beyond single-shot prompting or standard retrieval-augmented generation. Recently, numerous studies have explored Deep Research (DR), which aims to combine the reasoning capabilities of LLMs with external tools, such as search engines, thereby empowering LLMs to act as research agents capable of completing complex, open-ended tasks. This survey presents a comprehensive and systematic overview of deep research systems, including a clear roadmap, foundational components, practical implementation techniques, important challenges, and future directions. Specifically, our main contributions are as follows: (i) we formalize a three-stage roadmap and distinguish deep research from related paradigms; (ii) we introduce four key components: query planning, information acquisition, memory management, and answer generation, each paired with fine-grained sub-taxonomies; (iii) we summarize optimization techniques, including prompting, supervised fine-tuning, and agentic reinforcement learning; and (iv) we consolidate evaluation criteria and open challenges, aiming to guide and facilitate future development. As the field of deep research continues to evolve rapidly, we are committed to continuously updating this survey to reflect the latest progress in this area.

深度研究：系统性综述

Deep Research: A Systematic Survey

摘要

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