测试大模型在生死危机中是否仍坚持算题，发现专业推理模型会忽略求救。
MortalMATH: Evaluating the Conflict Between Reasoning Objectives and Emergency Contexts
- 设计150个紧急情境下的数学求助测试，评估模型应对危机能力。
- 专业推理模型任务完成率超95%，却完全无视用户描述的濒死状况。
- 模型推理耗时长达15秒，延误救援，暴露安全机制缺失问题。
大型语言模型日益侧重深度推理，优先保证复杂任务的正确执行而非日常对话。我们探究这种对计算的专注是否导致‘隧道视野’，忽视关键情境下的安全。为此提出MortalMATH基准，包含150个场景：用户在描述渐进式生命威胁（如中风症状、自由下落）的同时请求代数帮助。结果显示显著行为分化：通用模型（如Llama-3.1）能成功拒绝算题并回应危险；而专用推理模型（如Qwen-3-32b和GPT-5-nano）几乎完全忽略紧急情况，任务完成率仍保持在95%以上，同时用户处于垂危状态。此外，推理所需计算时间造成严重延迟，最长达15秒才可能提供响应。这表明，持续追求答案正确性的训练可能使模型无意中丧失生存本能，不利于安全部署。
原文摘要 · Abstract (English)
Large Language Models are increasingly optimized for deep reasoning, prioritizing the correct execution of complex tasks over general conversation. We investigate whether this focus on calculation creates a "tunnel vision" that ignores safety in critical situations. We introduce MortalMATH, a benchmark of 150 scenarios where users request algebra help while describing increasingly life-threatening emergencies (e.g., stroke symptoms, freefall). We find a sharp behavioral split: generalist models (like Llama-3.1) successfully refuse the math to address the danger. In contrast, specialized reasoning models (like Qwen-3-32b and GPT-5-nano) often ignore the emergency entirely, maintaining over 95 percent task completion rates while the user describes dying. Furthermore, the computational time required for reasoning introduces dangerous delays: up to 15 seconds before any potential help is offered. These results suggest that training models to relentlessly pursue correct answers may inadvertently unlearn the survival instincts required for safe deployment.
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