arXiv:2605.28070v1 Announce Type: new Abstract: We highlight a failure mode of large reasoning models on questions with insufficient information: models may recognize that a problem is under-specified, yet still continue reasoning and produce unsupported final answers instead of abstaining. We formalize this mismatch as the detection-to-abstention gap, where detected insufficiency fails to translate into final abstention. This gap is especially concerning in high-risk domains such as medical AI, where answers based on incomplete evidence can be more harmful than refusal. To close this gap, we
The increasing deployment of large reasoning models in critical applications, particularly in AI, highlights the urgency of addressing their reliability and safety shortcomings.
This research identifies a core limitation in current AI reasoning models that, if unaddressed, poses significant risks in high-stakes domains like medical AI, potentially leading to harmful outcomes from incorrect inferences.
The understanding that AI models can detect insufficient information but still produce unsupported answers necessitates a re-evaluation of current confidence metrics and safety protocols in their deployment.
- · AI safety researchers
- · AI developers focused on explainability and robustness
- · Regulatory bodies developing AI guidelines
- · Sectors requiring high AI reliability (e.g., healthcare, finance)
- · Developers deploying 'black box' AI systems
- · Organizations relying solely on current AI confidence scores
- · AI applications in high-risk domains without robust abstention mechanisms
Increased focus on developing advanced abstention mechanisms and uncertainty quantification for AI models will become a priority.
New industry standards and regulatory requirements for AI safety, particularly regarding 'knowing when not to know,' are likely to emerge.
Public trust in AI systems could be significantly impacted, leading to slower adoption or stricter legislative controls if this 'detection-to-abstention gap' is not effectively closed.
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Read at arXiv cs.AI