@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://preview.aclanthology.org/ingest-acl/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."
}