融合检索与推理的智能系统，让大模型更准更可信地回答复杂问题。
Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
- 将推理嵌入检索流程，提升每一步生成的逻辑性。
- 利用多类型外部知识补全推理前提，支持复杂推断。
- 适合需要深度思考与事实验证的研究者和开发者。
检索增强生成（RAG）通过引入外部知识提升大语言模型（LLM）的事实准确性，但在需要多步推理的问题上表现不足；而纯推理方法常产生幻觉或事实偏差。本综述从统一的推理-检索视角整合两类方法：首先，展示先进推理如何优化RAG各阶段（推理增强型RAG）；其次，阐明不同类型检索知识如何补充推理缺失前提并扩展上下文以支持复杂推理（RAG增强型推理）；最后，聚焦新兴的协同式RAG-推理框架，其中（代理型）LLM通过迭代式搜索与推理，在知识密集型基准测试中达到顶尖性能。我们对方法、数据集与开放挑战进行分类梳理，并提出迈向更高效、多模态适应、可信且以人为中心的深层RAG-推理系统的研究方向。资源详见 https://github.com/DavidZWZ/Awesome-RAG-Reasoning。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts. This survey synthesizes both strands under a unified reasoning-retrieval perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and reasoning to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric. The collection is available at https://github.com/DavidZWZ/Awesome-RAG-Reasoning.
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