Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning
  Systems in LLMs
Authors: name: Yangning Li; name: Weizhi Zhang; name: Yuyao Yang; name: Wei-Chieh Huang; name: Yaozu Wu; name: Junyu Luo; name: Yuanchen Bei; name: Henry Peng Zou; name: Xiao Luo; name: Yusheng Zhao; name: Chunkit Chan; name: Yankai Chen; name: Zhongfen Deng; name: Yinghui Li; name: Hai-Tao Zheng; name: Dongyuan Li; name: Renhe Jiang; name: Ming Zhang; name: Yangqiu Song; name: Philip S. Yu
Published: 2025-07-13 03:29:41

Abstract: 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.