@inproceedings{lu-etal-2026-streaming,
title = "Streaming Hallucination Detection in Long Chain-of-Thought Reasoning",
author = "Lu, Haolang and
Pan, Minghui and
LI, Ripeng and
Nan, Guoshun and
Zhuang, Jialin and
Zhao, Zijie and
Sun, Zhongxiang and
Wang, Kun and
Liu, Yang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1064/",
pages = "21157--21183",
ISBN = "979-8-89176-395-1",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Streaming Hallucination Detection in Long Chain-of-Thought Reasoning
%A Lu, Haolang
%A Pan, Minghui
%A LI, Ripeng
%A Nan, Guoshun
%A Zhuang, Jialin
%A Zhao, Zijie
%A Sun, Zhongxiang
%A Wang, Kun
%A Liu, Yang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lu-etal-2026-streaming
%X 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.
%U https://aclanthology.org/2026.findings-acl.1064/
%P 21157-21183
Markdown (Informal)
[Streaming Hallucination Detection in Long Chain-of-Thought Reasoning](https://aclanthology.org/2026.findings-acl.1064/) (Lu et al., Findings 2026)
ACL
- Haolang Lu, Minghui Pan, Ripeng LI, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, and Yang Liu. 2026. Streaming Hallucination Detection in Long Chain-of-Thought Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 21157–21183, San Diego, California, United States. Association for Computational Linguistics.