近年来，大模型的应用正从对话与创意写作，走向更加开放、复杂的研究型问题。尽管以检索增强生成（RAG）为代表的方法缓解了知识获取瓶颈，但其静态的 "一次检索 + 一次生成" 范式，难以支撑多步推理与长期研究流程，由此催生了 Deep Research（DR）这一新方向。 然而，随着相关工作的快速涌现，DR的概念也在迅速膨胀并趋于碎片化：不同工作在系统实现、任务假设与评价上差异显著；相似术语的使用进一步模糊了其能 力边界。 正是在这一背景下，来自山东大学、清华大学、CMU、UIUC、腾讯等机构共同撰写并发布了目前最全面的深度研究智能体综述《Deep Research: A Systematic Survey》。文章首先提出一条由浅入深的三阶段能力发展路径，随后从系统视角系统化梳理关键组件，并进一步总结了对应的训练与优化方法。 什么是 Deep Research DR 并非某一具体模型或技术，而是一条逐步演进的能力路径。综述刻画了研究型智能体从信息获取到完整科研流程的能力提升过程。基于对现有工作的梳理，可 将这一演进划分为三个阶段。 阶段 1：「Agentic Search」。模型开始具备主动搜索与多步信息获取能力 ...

Group 1: Development Trends of Large Models - The rapid development of large models in the AI field is transforming the understanding of AI and advancing the dream of AGI (Artificial General Intelligence) from science fiction to reality, characterized by two core trends: continuous leaps in model capabilities and increasing openness of model ecosystems [1][4]. - Continuous improvement in model capabilities is achieved through iterative advancements and technological innovations, with examples like OpenAI's ChatGPT series showing significant enhancements in language understanding and generation from GPT-3.5 to GPT-4 [1][2]. - The breakthrough in multimodal capabilities allows models to natively support various data types, including text, audio, images, and video, enabling more natural and rich interactions [2][3]. Group 2: Evolution of AI Applications - The rapid advancement of large model capabilities is driving profound changes in AI application forms, evolving from conversational AI to systems capable of human-level problem-solving [5][6]. - The emergence of AI agents, which can take actions on behalf of users and interact with external environments through tool usage, marks a significant evolution in AI applications [6][8]. - The recent surge in AI agents, both general and specialized, demonstrates their potential in solving a wide range of tasks and enhancing efficiency in various domains [8][9]. Group 3: AI Middleware Opportunities and Challenges - AI middleware is emerging as a crucial layer that connects foundational large models with specific applications, offering opportunities for agent development efficiency, context engineering, memory management, and tool usage [13][19][20]. - The challenges faced by AI middleware include managing complex contexts, updating and utilizing persistent memory, optimizing retrieval-augmented generation (RAG) effects, and ensuring safe tool usage [26][29][30]. - The future of AI middleware is expected to focus on scaling AI applications, providing higher-level abstractions, and integrating AI into business processes, ultimately becoming the "nervous system" of organizations [39][40].