Computer Science / Artificial Intelligence

Streaming Hallucination Detection in Long Chain-of-Thought Reasoning

February 09, 2026

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Title: Streaming Hallucination Detection in Long Chain-of-Thought Reasoning
ArXiv ID: 2601.02170
Date: 2026-01-05
Authors: Haolang Lu, Minghui Pan, Ripeng Li, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu

📝 Abstract

Long chain-of-thought (CoT) reasoning improves the performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. We suggest that hallucination in long CoT reasoning is better understood as an evolving latent state rather than a one-off erroneous event. Accordingly, we treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level hallucination signal that tracks the global evolution of the reasoning state over the entire trajectory. Overall, our approach enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence. 1

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Streaming Hallucination Detection in Long Chain-of-Thought Reasoning

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