REDE paper filters noisy reasoning traces to improve hallucination detection
SharonYixuanLi · x · 2026-07-29
REDE filters noisy reasoning traces to improve hallucination detection
Large reasoning models often produce long traces that include irrelevant or repetitive steps, and those noisy steps can obscure signals useful for hallucination detection.
The paper identifies two common noise types — irrelevant steps and repetitive steps — and shows that simple confidence scores or embedding-based filtering do not reliably separate noisy from informative reasoning steps. REDE addresses this by using final-answer attention as automatic supervision to shape step-level representations, making noisy steps easier to identify and remove.
In experiments across multiple reasoning benchmarks, REDE consistently improves hallucination detection over competitive baselines, and it can be plugged into different detectors by operating on the filtered trajectory.
Related event: REDE Denoises Reasoning Traces to Detect LLM Hallucinations(2 posts)→
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