系统学习Deep Research，这一篇综述就够了

Core Insights - The article discusses the evolution of Deep Research (DR) as a new direction in AI, moving from simple dialogue and creative writing applications to more complex research-oriented tasks. It highlights the limitations of traditional retrieval-augmented generation (RAG) methods and introduces DR as a solution for multi-step reasoning and long-term research processes [2][30]. Summary by Sections Definition of Deep Research - DR is not a specific model or technology but a progressive capability pathway for research-oriented agents, evolving from information retrieval to complete research workflows [5]. Stages of Capability Development - Stage 1: Agentic Search - Models gain the ability to actively search and retrieve information dynamically based on intermediate results, focusing on efficient information acquisition [5]. - Stage 2: Integrated Research - Models evolve to understand, filter, and integrate multi-source evidence, producing coherent reports [6]. - Stage 3: Full-stack AI Scientist - Models can propose research hypotheses, design and execute experiments, and reflect on results, emphasizing depth of reasoning and autonomy [6]. Core Components of Deep Research - Query Planning - Involves deciding what information to query next, incorporating dynamic adjustments in multi-round research [10]. - Information Retrieval - Focuses on when to retrieve, what to retrieve, and how to filter retrieved information to avoid redundancy and ensure relevance [12][13][14]. - Memory Management - Essential for long-term reasoning, involving memory consolidation, indexing, updating, and forgetting [15]. - Answer Generation - Stresses the logical consistency between conclusions and evidence, requiring integration of multi-source evidence [17]. Training and Optimization Methods - Prompt Engineering - Involves designing multi-step prompts to guide the model through research processes, though its effectiveness is highly dependent on prompt design [20]. - Supervised Fine-tuning - Utilizes high-quality reasoning trajectories for model training, though acquiring annotated data can be costly [21]. - Reinforcement Learning for Agents - Directly optimizes decision-making strategies in multi-step processes without complex annotations [22]. Challenges in Deep Research - Coordination of Internal and External Knowledge - Balancing reliance on internal reasoning versus external information retrieval is crucial [24]. - Stability of Training Algorithms - Long-term task training often faces issues like policy degradation, limiting exploration of diverse reasoning paths [24]. - Evaluation Methodology - Developing reliable evaluation methods for research-oriented agents remains an open question, with existing benchmarks needing further exploration [25][27]. - Memory Module Construction - Balancing memory capacity, retrieval efficiency, and information reliability is a significant challenge [28]. Conclusion - Deep Research represents a shift from single-turn answer generation to in-depth research addressing open-ended questions. The field is still in its early stages, with ongoing exploration needed to create autonomous and trustworthy DR agents [30].