@inproceedings{xu-etal-2026-checkrlm,
title = "{C}heck{RLM}: Effective Knowledge{--}Thought Coherence Checking in Retrieval-Augmented Reasoning",
author = "Xu, Dingling and
Wang, Ruobing and
Zhao, Qingfei and
Yan, Yukun and
Wang, Zhichun and
Zha, Daren and
Yu, Shi and
Liu, Zhenghao and
Wang, Shuo and
Han, Xu and
Sun, Maosong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1780/",
pages = "38403--38426",
ISBN = "979-8-89176-390-6",
abstract = "Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are prone to containing factual errors, particularly in knowledge-intensive tasks. To address this issue, we propose **CheckRLM**, a framework that improves the reliability of the reasoning process through Retrieval-Augmented Generation (RAG) by timely checking and correcting factual errors. Specifically, CheckRLM extracts factual claims from the reasoning chain to identify and localize subtle knowledge inconsistencies during inference. Upon detection of errors, a refinement mechanism performs minimal-cost yet precise corrections by leveraging external knowledge, ensuring coherence between the reasoning chain and correct knowledge. Extensive experiments demonstrate that CheckRLM substantially outperforms existing baselines, exhibiting a strong capability to mitigate error accumulation in long-horizon reasoning with lower costs. The code and data are available at https://github.com/AI9Stars/CheckRLM."
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%0 Conference Proceedings
%T CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning
%A Xu, Dingling
%A Wang, Ruobing
%A Zhao, Qingfei
%A Yan, Yukun
%A Wang, Zhichun
%A Zha, Daren
%A Yu, Shi
%A Liu, Zhenghao
%A Wang, Shuo
%A Han, Xu
%A Sun, Maosong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F xu-etal-2026-checkrlm
%X Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are prone to containing factual errors, particularly in knowledge-intensive tasks. To address this issue, we propose **CheckRLM**, a framework that improves the reliability of the reasoning process through Retrieval-Augmented Generation (RAG) by timely checking and correcting factual errors. Specifically, CheckRLM extracts factual claims from the reasoning chain to identify and localize subtle knowledge inconsistencies during inference. Upon detection of errors, a refinement mechanism performs minimal-cost yet precise corrections by leveraging external knowledge, ensuring coherence between the reasoning chain and correct knowledge. Extensive experiments demonstrate that CheckRLM substantially outperforms existing baselines, exhibiting a strong capability to mitigate error accumulation in long-horizon reasoning with lower costs. The code and data are available at https://github.com/AI9Stars/CheckRLM.
%U https://aclanthology.org/2026.acl-long.1780/
%P 38403-38426
Markdown (Informal)
[CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning](https://aclanthology.org/2026.acl-long.1780/) (Xu et al., ACL 2026)
ACL
- Dingling Xu, Ruobing Wang, Qingfei Zhao, Yukun Yan, Zhichun Wang, Daren Zha, Shi Yu, Zhenghao Liu, Shuo Wang, Xu Han, and Maosong Sun. 2026. CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 38403–38426, San Diego, California, United States. Association for Computational Linguistics.