Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning
  Systems in LLMs
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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Yangning Li
68787030001546c83aa4f9af
Weizhi Zhang
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2025-07-17T08:22:48.379Z
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Weizhi Zhang
user
WZDavid
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Yuyao Yang
68787030001546c83aa4f9b1
Wei-Chieh Huang
68787030001546c83aa4f9b2
Yaozu Wu
68787030001546c83aa4f9b3
Junyu Luo
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Yuanchen Bei
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Henry Peng Zou
claimed_verified
2025-07-17T08:22:46.466Z
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Henry Peng Zou
user
TreeForest
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Xiao Luo
68787030001546c83aa4f9b7
Yusheng Zhao
68787030001546c83aa4f9b8
Chunkit Chan
68787030001546c83aa4f9b9
Yankai Chen
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Zhongfen Deng
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Yinghui Li
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Hai-Tao Zheng
68787030001546c83aa4f9bd
Dongyuan Li
68787030001546c83aa4f9be
Renhe Jiang
68787030001546c83aa4f9bf
Ming Zhang
68787030001546c83aa4f9c0
Yangqiu Song
68787030001546c83aa4f9c1
Philip S. Yu
Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning
  Systems in LLMs
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.
This survey integrates reasoning and retrieval in Large Language Models to improve factuality and multi-step inference, highlighting Synergized RAG-Reasoning frameworks and outlining future research directions.
Retrieval-Augmented Generation Large Language Models Reasoning-Enhanced RAG RAG-Enhanced Reasoning Synergized RAG-Reasoning knowledge-intensive benchmarks multimodally-adaptive trustworthy human-centric