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TC–RAG: Turing–Complete RAG's Case study on Medical LLM Systems

Paper · 2025 · Xinke Jiang, Yue Fang, Rihong Qiu et al.

Summary

Retrieval-augmented generation can reduce hallucinations and stale knowledge in medical LLMs. However, existing RAG pipelines lack an explicit state variable that tells the system when to stop retrieving, so they can loop without converging or pile up noisy context. The authors propose TC–RAG, which they prove to be Turing Complete. It adds a monitored system state to a memory-stack architecture. The LLM decides whether and what to retrieve, plans which tools to use, and manipulates the stack through Push and Pop operations. Backtrack removes harmful knowledge, Summary condenses redundant content, and the state monitor decides when to halt. To support expert-level planning, the team gathered Chinese medical data, pre-trained a medical LLM, and built document collections and a knowledge graph. Across seven medical and general-domain Q&A datasets, TC–RAG beats prior RAG baselines by more than 7.20% in accuracy while using less time and fewer tokens than other iterative methods. The work points toward controllable, convergent retrieval for high-stakes domains. The system has reportedly been deployed on an online hospital platform.

Key points

  • The core mechanism is a memory stack controlled by a state variable. It supports Push, Pop, Backtrack and Summary actions, and the authors present a formal argument that the system is Turing Complete. Two variants are reported: TC–RAG-cppl and TC–RAG-uct.
  • Ablations show every component helps. Removing the State Monitor causes the largest drop, because termination then depends only on the LLM's own "Final Answer" action, making the system a black box.
  • Efficiency results come from Qwen-32B on CMB-Exam. TC–RAG averages 4.78 interactions, 3.37 retrievals, 50.91 s and 458.82 tokens per query. By comparison, DRAGIN needs 167.95 s and 998.64 tokens, and ReACT needs 130.87 s and 874.29 tokens.
  • The released resources include a curated Chinese medical pre-training dataset, medical documents and a knowledge graph, available at github.com/Artessay/TC-RAG.
  • The authors acknowledge two limitations: computational overhead remains notable despite speculative sampling, and the framework is tuned for general medicine rather than narrow specialties.

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

 Institutions

🎓Peking UniversityBeijing · China244 works · 39,359 citations🎓University of Chinese Academy of SciencesBeijing · China147 works · 8,480 citations🎓University of Electronic Science and Technology of ChinaChengdu · China31 works · 2,012 citations🎓ShanghaiTech UniversityShanghai · China13 works · 1,974 citations

Also on the byline: City University of Hong Kong (Dongguan).

 Fields, topics and concepts

📚 Shelves 5The 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 worksInformation Systems · 1,437 works

Cite

Cite as Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao and Yasha Wang. 2025. TC–RAG: Turing–Complete RAG's Case study on Medical LLM Systems. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.558

@inproceedings{jiang2025tc,
  title = {{TC–RAG: Turing–Complete RAG's Case study on Medical LLM Systems}},
  author = {Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao and Yasha Wang},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.558},
  url = {https://doi.org/10.18653/v1/2025.acl-long.558}
}

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