Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
It tackles the problem of improving factuality and reasoning in large language models for AI researchers and practitioners, but it is incremental as a survey rather than introducing new methods.
This survey addresses the limitations of Retrieval-Augmented Generation (RAG) in multi-step inference and reasoning approaches in hallucination by synthesizing them into a unified reasoning-retrieval framework, highlighting emerging synergistic systems that achieve state-of-the-art performance on knowledge-intensive benchmarks.
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.