@inproceedings{jiang-etal-2025-tc,
    title = "{TC}{--}{RAG}: {T}uring{--}Complete {RAG}{'}s Case study on Medical {LLM} Systems",
    author = "Jiang, Xinke  and
      Fang, Yue  and
      Qiu, Rihong  and
      Zhang, Haoyu  and
      Xu, Yongxin  and
      Chen, Hao  and
      Zhang, Wentao  and
      Zhang, Ruizhe  and
      Fang, Yuchen  and
      Ma, Xinyu  and
      Chu, Xu  and
      Zhao, Junfeng  and
      Wang, Yasha",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.558/",
    pages = "11400--11426",
    ISBN = "979-8-89176-251-0",
    abstract = "In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized queries. However, existing approaches to RAG fall short by neglecting system state variables, which are crucial for ensuring adaptive control, retrieval halting, and system convergence. In this paper, we introduce the Turing-Complete-RAG (TC-RAG) through rigorous proof, a novel framework that addresses these challenges by incorporating a Turing Complete System to manage state variables, thereby enabling more efficient and accurate knowledge retrieval. By leveraging a memory stack system with adaptive retrieval, reasoning, and planning capabilities, TC-RAG not only ensures the controlled halting of retrieval processes but also mitigates the accumulation of erroneous knowledge via Push and Pop actions. In the case study of the medical and general domain, our extensive experiments on seven real-world healthcare and general-domain datasets demonstrate the superiority of TC-RAG over existing methods in accuracy by over 7.20{\%}. Our code, datasets and RAG resources have been available at https://github.com/Artessay/TC-RAG."
}