{"ID":2895128,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09477","arxiv_id":"2507.09477","title":"Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.09477","url_pdf":"https://arxiv.org/pdf/2507.09477v2","authors":"[\"Yangning Li\",\"Weizhi Zhang\",\"Yuyao Yang\",\"Wei-Chieh Huang\",\"Yaozu Wu\",\"Junyu Luo\",\"Yuanchen Bei\",\"Henry Peng Zou\",\"Xiao Luo\",\"Yusheng Zhao\",\"Chunkit Chan\",\"Yankai Chen\",\"Zhongfen Deng\",\"Yinghui Li\",\"Hai-Tao Zheng\",\"Dongyuan Li\",\"Renhe Jiang\",\"Ming Zhang\",\"Yangqiu Song\",\"Philip S. Yu\"]","published":"2025-07-13T03:29:41Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":612158,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2895128,"paper_url":"https://arxiv.org/abs/2507.09477","paper_title":"Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs","repo_url":"https://github.com/DavidZWZ/Awesome-RAG-Reasoning","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}