Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models
Large reasoning models (LRMs) generate long reasoning traces before producing final answers. While these traces may contain useful signals for hallucination detection, harnessing them is non-trivial because long trajectories often include noisy steps that obscure the cues relevant to truthfulness assessment. In this paper, we identify two prevalent forms of reasoning noises, i.e., irrelevant steps and repetitive steps, and show that both substantially degrade hallucination detection performance. Existing confidence-based scores and naive embedding-based filtering fail to reliably separate noisy from informative steps. To address this challenge, we propose REDE, a novel learning framework ...
| Authors | Junlin Fang, Do Nguyen-Thanh, Xiaogang Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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