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News/Quantifying and Mitigating Premature Closure in Frontier LLMs
arxiv
PublishedMay 16, 2026 at 4:00 AM
▼bearish

Quantifying and Mitigating Premature Closure in Frontier LLMs

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Publisher summary· verbatim

arXiv:2605.15000v1 Announce Type: cross Abstract: Premature closure, or committing to a conclusion before sufficient information is available, is a recognized contributor to diagnostic error but remains underexamined in large language models (LLMs). We define LLM premature closure as inappropriate c

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Mentioned models
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Read original ↗All from arxiv →
Tags
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#safety#evaluation#language-models#medical-ai

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