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

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.