{
  "date": "2026-10-03",
  "papers": [
    {
      "url": "https://arxiv.org/abs/2606.15367",
      "title": "S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents",
      "authors": "Yao Dong, Xinglin Xiao, Liwei Dong, Xinlong Jin, Zhengbo Li, Heng Zhang, Duyun Wang, Nan Xu",
      "date": "2026-06-13",
      "abstract": "Deep research agents aim to solve complex knowledge-intensive tasks through long-horizon planning, evidence gathering, reasoning, and report generation. While recent progress in search agents has demonstrated strong capabilities in information retrieval and answer verification, most existing training datasets remain search-centric, focusing primarily on closed-ended question answering and information localization. As a result, they mainly train information-seeking behavior while providing limited coverage of key deep research capabilities, including evidence integration, knowledge synthesis, planning, file understanding, and structured report generation. In this work, we propose a unified trajectory construction paradigm for deep research agents that combines closed-ended QA and open-ended exploration. Compared with existing search-oriented datasets, our synthesized trajectories place greater emphasis on knowledge synthesis, complex reasoning, and planning. S1-DeepResearch-32B achieves state-of-the-art performance among open-source models of comparable scale."
    },
    {
      "url": "https://arxiv.org/abs/2504.03160",
      "title": "DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments",
      "authors": "Yuxiang Zheng, Dayuan Fu, Xiangkun Hu, Xiaojie Cai, Lyumanshan Ye, Pengrui Lu, Pengfei Liu",
      "date": "2025-04-04",
      "abstract": "Large Language Models (LLMs) equipped with web search capabilities have demonstrated impressive potential for deep research tasks. However, current approaches predominantly rely on either manually engineered prompts (prompt engineering-based) with brittle performance or reinforcement learning within controlled Retrieval-Augmented Generation (RAG) environments (RAG-based) that fail to capture the complexities of real-world interaction. In this paper, we introduce DeepResearcher, the first comprehensive framework for end-to-end training of LLM-based deep research agents through scaling reinforcement learning (RL) in real-world environments with authentic web search interactions. Unlike RAG-based approaches that assume all necessary information exists within a fixed corpus, our method trains agents to navigate the noisy, unstructured, and dynamic nature of the open web. We release DeepResearcher at https://github.com/GAIR-NLP/DeepResearcher."
    },
    {
      "url": "https://arxiv.org/abs/2604.07720",
      "title": "Towards Knowledgeable Deep Research: Framework and Benchmark",
      "authors": "Wenxuan Liu, Zixuan Li, Long Bai, Chunmao Zhang, Fenghui Zhang, Zhuo Chen, Wei Li, Yuxin Zuo, Fei Wang, Bingbing Xu, Xuhui Jiang, Jin Zhang, Xiaolong Jin, Jiafeng Guo, Tat-Seng Chua, Xueqi Cheng",
      "date": "2026-04-09",
      "abstract": "Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive reports. In contrast to existing studies that mainly focus on unstructured web content, a more challenging DR task should additionally utilize structured knowledge to provide a solid data foundation, facilitate quantitative computation, and lead to in-depth analyses. In this paper, we refer to this novel task as Knowledgeable Deep Research (KDR), which requires DR agents to generate reports with both structured and unstructured knowledge. Furthermore, we propose the Hybrid Knowledge Analysis framework (HKA), a multi-agent architecture that reasons over both kinds of knowledge and integrates the texts, figures, and tables into coherent multimodal reports."
    },
    {
      "url": "https://arxiv.org/abs/2512.02038",
      "title": "Deep Research: A Systematic Survey",
      "authors": "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, Yiming Yang, Zhaopeng Tu, Zhaochun Ren",
      "date": "2025-12-24",
      "abstract": "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."
    },
    {
      "url": "https://arxiv.org/abs/2603.28361",
      "title": "Deep Research of Deep Research: From Transformer to Agent, From AI to AI for Science",
      "authors": "Yipeng Yu",
      "date": "2026-03-30",
      "abstract": "Deep research (DR) represents a prototypical vertical application for general-purpose agents, which represents an ideal approach for intelligent information processing and assisting humans in discovering and solving problems, with the goal of reaching or even surpassing the level of top human scientists. This paper provides a deep research of deep research. We articulate a clear and precise definition of deep research and unify perspectives from industry's deep research and academia's AI for Science (AI4S) within a developmental framework. We position LLMs and Stable Diffusion as the twin pillars of generative AI, and lay out a roadmap evolving from the Transformer to agents."
    }
  ],
  "repo": {
    "url": "https://github.com/GAIR-NLP/DeepResearcher",
    "name": "GAIR-NLP/DeepResearcher",
    "description": "DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments",
    "graft_analysis_path": "profiles/r-and-d/raw_evidence/deepresearcher_graft_map.txt",
    "stats": "264 files, 1897 symbols, 5263 edges, ~489605 tokens saved via graft"
  }
}