[2605.03050] Evaluating Reasoning Models for Queries with Presuppositions
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Computer Science > Computation and Language
arXiv:2605.03050 (cs)
[Submitted on 4 May 2026]
Title:Evaluating Reasoning Models for Queries with Presuppositions
Authors:Rose Sathyanathan, Kinshuk Vasisht, Danish Pruthi
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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.
Comments: Accepted to ACL 2026 (Findings)
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.03050 [cs.CL]
(or arXiv:2605.03050v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.03050
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Rose Sathyanathan [view email] [v1] Mon, 4 May 2026 18:15:28 UTC (138 KB)
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