Streaming Hallucination Detection in Long Chain-of-Thought Reasoning
This addresses hallucination issues in long reasoning chains for users of large language models, but it appears incremental as it builds on existing CoT frameworks.
The paper tackled the problem of hallucinations in long chain-of-thought reasoning by proposing a method to detect them as an evolving latent state, enabling real-time, interpretable detection.
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.