@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://aclanthology.org/2025.acl-long.558/",
doi = "10.18653/v1/2025.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(LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized queries.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."
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<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(LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized queries.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.</abstract>
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%0 Conference Proceedings
%T TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems
%A Jiang, Xinke
%A Fang, Yue
%A Qiu, Rihong
%A Zhang, Haoyu
%A Xu, Yongxin
%A Chen, Hao
%A Zhang, Wentao
%A Zhang, Ruizhe
%A Fang, Yuchen
%A Ma, Xinyu
%A Chu, Xu
%A Zhao, Junfeng
%A Wang, Yasha
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F jiang-etal-2025-tc
%X 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.
%R 10.18653/v1/2025.acl-long.558
%U https://aclanthology.org/2025.acl-long.558/
%U https://doi.org/10.18653/v1/2025.acl-long.558
%P 11400-11426
Markdown (Informal)
[TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems](https://aclanthology.org/2025.acl-long.558/) (Jiang et al., ACL 2025)
ACL
- 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. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11400–11426, Vienna, Austria. Association for Computational Linguistics.