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Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs

Paper · 2025 · Yangning Li, Weizhi Zhang, Yuyao Yang et al.

Summary

Large language models face two linked weaknesses. Their stored knowledge is fixed and can be wrong, and they struggle with problems that need several reasoning steps. Retrieval-Augmented Generation (RAG) addresses the first, and reasoning techniques such as Chain-of-Thought address the second. This survey argues that each weakness worsens the other, so the two should be studied together. It reviews over 200 papers and sorts them into three groups. The first uses reasoning to improve RAG's retrieval, integration and generation stages. The second uses retrieved knowledge to fill factual gaps during reasoning. The third, called Synergized RAG-Reasoning, has agentic models alternate searching and thinking in a loop. The authors note that one-way methods follow a fixed retrieve-then-reason pipeline, which limits how adaptive they can be. They conclude that interleaved systems, like commercial "Deep Research" tools, give better factual grounding and coherence. The survey also lists benchmarks and open problems. It offers a map for building more trustworthy, multimodal and human-centric retrieval-reasoning systems.

Key points

  • Taxonomy: The paper has three branches.
    • Reasoning→RAG covers query reformulation, retrieval planning, retriever enhancement, filtering and fusion, and grounded generation.
    • RAG→Reasoning covers knowledge bases, web search, tool use, prior experience, and examples.
    • RAG⇔Reasoning covers chain-, tree- and graph-based workflows, plus single- and multi-agent orchestration.
  • Example methods by category: IRCoT, MCTS-RAG and ToG (workflows); ReAct and Search-O1 (prompting); Toolformer (SFT); Search-R1 and R1-Searcher (RL); HM-RAG and Chain of Agents (multi-agent).
  • Benchmarks catalogued:
    • BrowseComp (1,266) and GAIA (466) for web browsing.
    • TriviaQA (650,000+) and NQ (307,373) for single-hop QA.
    • 2WikiMultiHopQA (192,606), HotpotQA (113,000) and MuSiQue (25,000) for multi-hop QA.
    • Also MATH, AQuA, MMLU-Pro, QuALITY and LiveCodeBench.
  • Open challenges: retrieval trustworthiness (e.g., poisoned sources), true multimodality with unified retrievers, efficiency, and human-centric design.
  • Resource: The paper list is maintained at github.com/DavidZWZ/Awesome-RAG-Reasoning.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 worksInformation Systems · 1,437 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as 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 and Philip S. Yu. 2025. Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs. arXiv:2507.09477.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2507.09477 · arXiv 2507.09477

@misc{li2025agentic,
  title = {{Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs}},
  author = {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 and Philip S. Yu},
  year = {2025},
  doi = {10.48550/arxiv.2507.09477},
  eprint = {2507.09477},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2507.09477}
}

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