Deep Research: A Systematic Survey
Zhengliang Shi, Yiqun Chen, Haitao Li, Weiwei Sun, Shiyu Ni, Yougang Lyu, Run-Ze Fan, Bowen Jin, Yixuan Weng, Minjun Zhu, Qiujie Xie, Xinyu Guo, Qu Yang, Jiayi Wu, Jujia Zhao, Xiaqiang Tang, Xinbei Ma, Cunxiang Wang, Jiaxin Mao, Qingyao Ai, Jen-Tse Huang, Wenxuan Wang, Yue Zhang

TL;DR
This survey systematically reviews deep research systems that combine large language models with external tools, outlining components, techniques, challenges, and future directions for complex open-ended tasks.
Contribution
It formalizes a three-stage roadmap, introduces key components and sub-taxonomies, and consolidates evaluation criteria and challenges in deep research with LLMs.
Findings
Defines a three-stage deep research roadmap
Identifies four key components with sub-taxonomies
Summarizes optimization and evaluation techniques
Abstract
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;…
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Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Artificial Intelligence in Healthcare and Education
