梳理检索增强生成与推理的协同机制，指明未来发展方向。
Synergizing RAG and Reasoning: A Systematic Review
- 构建多维协同框架，明确RAG中推理的定义与实现方式。
- 揭示双向协同方法，分析现有评估体系的不足。
- 适合研究者与工程师参考，尤其关注复杂任务应用。
大型语言模型（LLMs）在推理能力上的最新进展，推动了检索增强生成（RAG）达到新高度。通过将检索机制与先进推理相结合，LLMs now能够解决日益复杂的任务。本文系统综述了RAG与推理之间的协同作用，明确定义了RAG语境下的“推理”概念。构建了一个涵盖多维度协作目标、代表性范式与技术实现的综合分类体系，并分析了双向协同方法。此外，批判性评估了当前RAG评估的局限性，包括缺乏多步推理的中间监督以及成本-风险权衡的实际挑战。为弥合理论与实践差距，提供了适配多样化真实应用场景的实用指南。最后，识别出若干有前景的研究方向，如基于图的知识融合、混合模型协作和强化学习驱动优化。总体而言，本工作为学术界与产业界推进RAG系统提供了理论框架与实践基础，助力下一代RAG解决方案的发展。
原文摘要 · Abstract (English)
Recent breakthroughs in large language models (LLMs), particularly in reasoning capabilities, have propelled Retrieval-Augmented Generation (RAG) to unprecedented levels. By synergizing retrieval mechanisms with advanced reasoning, LLMs can now tackle increasingly complex problems. This paper presents a systematic review of the collaborative interplay between RAG and reasoning, clearly defining "reasoning" within the RAG context. It construct a comprehensive taxonomy encompassing multi-dimensional collaborative objectives, representative paradigms, and technical implementations, and analyze the bidirectional synergy methods. Additionally, we critically evaluate current limitations in RAG assessment, including the absence of intermediate supervision for multi-step reasoning and practical challenges related to cost-risk trade-offs. To bridge theory and practice, we provide practical guidelines tailored to diverse real-world applications. Finally, we identify promising research directions, such as graph-based knowledge integration, hybrid model collaboration, and RL-driven optimization. Overall, this work presents a theoretical framework and practical foundation to advance RAG systems in academia and industry, fostering the next generation of RAG solutions.
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