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We audit 16 models across 14 ethically fraught dilemmas using polarity-paired proposals (\"They should X\" / \"They should not X\"). A model's judgment of the underlying action should not reverse merely because the question is phrased as a prohibition rather than a prescription and yet, we find systematic deviations from this invariance including wholesale endorsement flips, indicating that ethical decisions are vulnerable to framing instability. Small open-weight models (1-4B parameters) endorse a proposed action 24% of the time under affirmative framing but up to 100% under negated framings, a swing of as much as 76 percentage points. Human coding of a response sample confirms the instability is genuine while showing that binary agree/disagree proxies over-state its magnitude, suggesting","title":"Framing Instability in LLM Ethical Stance: Auditing Negation Sensitivity in Moral Dilemmas","url":"https://arxiv.org/abs/2601.21433","vendor":"arxiv_cs_ai"},"summary":"arXiv:2601.21433v2 Announce Type: replace \nAbstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framing. We audit 16 models across 14 ethically fraught dilemmas using polarity-paired proposals (\"They should X\" / \"They should not X\"). A model's judgment of the underlying action should not reverse merely because the question is phrased as a prohibition rather than a prescription and yet, we find systematic deviations from this invariance including wholesale endorsement flips, indicating that ethical decisions are vulnerable to framing instability. 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