@inproceedings{sathyanathan-etal-2026-evaluating,
    title = "Evaluating Reasoning Models for Queries with Presuppositions",
    author = "Sathyanathan, Rose  and
      Vasisht, Kinshuk  and
      Pruthi, Danish",
    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-workshops/2026.findings-acl.1201/",
    pages = "23991--24006",
    ISBN = "979-8-89176-395-1",
    abstract = "Millions of users turn to AI models for their information needs. It is conceivable that a large number of user queries contain assumptions that may be factually inaccurate. Prior work notes that large language models (LLMs) often fail to challenge such erroneous assumptions, and can reinforce users' misinformed opinions. However, given the recent advances, especially in model{'}s reasoning capabilities, we revisit whether large reasoning models (LRMs) can reason about the underlying assumptions and respond to user queries appropriately. We construct queries with varying degrees of presuppositions spanning health, science, and general knowledge, and use it to evaluate several widely-deployed models When compared to non-reasoning models, we find that reasoning models achieve a slightly higher accuracy (2-11{\%}), but they still fail to challenge a large fraction (26-42{\%}) of false presuppositions. Further, reasoning models remain susceptible to how strongly the presupposition is expressed."
}