Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| author | Li, Yangning Zhang, Weizhi Yang, Yuyao Huang, Wei-Chieh Wu, Yaozu Luo, Junyu Bei, Yuanchen Zou, Henry Peng Luo, Xiao Zhao, Yusheng Chan, Chunkit Chen, Yankai Deng, Zhongfen Li, Yinghui Zheng, Hai-Tao Li, Dongyuan Jiang, Renhe Zhang, Ming Song, Yangqiu Yu, Philip S. |
| author_facet | Li, Yangning Zhang, Weizhi Yang, Yuyao Huang, Wei-Chieh Wu, Yaozu Luo, Junyu Bei, Yuanchen Zou, Henry Peng Luo, Xiao Zhao, Yusheng Chan, Chunkit Chen, Yankai Deng, Zhongfen Li, Yinghui Zheng, Hai-Tao Li, Dongyuan Jiang, Renhe Zhang, Ming Song, Yangqiu Yu, Philip S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs Li, Yangning Zhang, Weizhi Yang, Yuyao Huang, Wei-Chieh Wu, Yaozu Luo, Junyu Bei, Yuanchen Zou, Henry Peng Luo, Xiao Zhao, Yusheng Chan, Chunkit Chen, Yankai Deng, Zhongfen Li, Yinghui Zheng, Hai-Tao Li, Dongyuan Jiang, Renhe Zhang, Ming Song, Yangqiu Yu, Philip S. Computation and Language Artificial Intelligence 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. |
| title | Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.09477 |