CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning
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 exter...
| Authors | Dingling Xu, Ruobing Wang, Qingfei Zhao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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