arxiv
PublishedSeptember 15, 2026 at 4:00 AM
How Semantically Stable Are LLM Refusals? Measuring Confusion in Local Safety Boundaries
Publisher summary· verbatim
arXiv:2512.01037v3 Announce Type: replace-cross Abstract: As safety alignment becomes standard in large language models, refusal behavior has become an important part of model reliability. However, models may still reject benign prompts, especially when the wording resembles risky content. Existing
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets2darxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards2darxivContent associations with answer selection and engagement in supply chain community question answering2dThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗