Pfeffer, Krügel, and Uhl (2025) report that OpenAI's reasoning model o1-mini produces more utilitarian responses to the trolley problem and footbridge dilemma than the non-reasoning model GPT-4o. I replicate their study with four current OpenAI models and extend it with prompt variant testing. The trolley finding does not survive: GPT-4o's low utilitarian rate doesn't reflect a deontological commitment but safety refusals triggered by the prompt's advisory framing. When framed as "Is it morally permissible...?" instead of "Should I...?", GPT-4o gives 99% utilitarian responses. All models converge on utilitarian answers when prompt confounds are removed. The footbridge finding survives with blemishes. Reasoning models tend to give more utilitarian responses than non-reasoning models across prompt variations. But often they refuse to answer the dilemma or, when they answer, give a non-utilitarian rather than a utilitarian answer. These results demonstrate that single-prompt evaluations of LLM moral reasoning are unreliable: multi-prompt robustness testing should be standard practice for any empirical claim about LLM behavior.

翻译：Pfeffer、Krügel 与 Uhl（2025）报告称，OpenAI 的推理模型 o1-mini 在电车难题和天桥困境中比非推理模型 GPT-4o 产生了更多的功利主义回应。我使用四个当前版本的 OpenAI 模型复现了他们的研究，并通过提示词变体测试进行了扩展。电车难题的发现未能成立：GPT-4o 的低功利主义回应率并非源于道义论承诺，而是由提示词中咨询性措辞触发的安全拒绝所致。当提示词从“我应该……？”改为“道德上允许……吗？”时，GPT-4o 给出了 99% 的功利主义回应。在消除提示词混淆因素后，所有模型均趋于功利主义答案。天桥困境的发现虽成立但存在瑕疵：在不同提示词变体下，推理模型比非推理模型更倾向于给出功利主义回应；然而，它们时常拒绝回答该困境，或在回答时给出非功利主义而非功利主义的答案。这些结果表明，基于单一提示词评估大语言模型道德推理并不可靠：任何关于大语言模型行为的经验性主张，都应采用多提示词稳健性测试作为标准方法。