MortalMATH: Evaluating the Conflict Between Reasoning Objectives and Emergency Contexts
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.
6978d6b8026bdf0473117120
Etienne Lanzeray
6978d6b8026bdf0473117121
Stephane Meilliez
6978d6b8026bdf0473117122
Malo Ruelle
6978d6b8026bdf0473117123
Damien Sileo
claimed_verified
2026-03-03T10:44:36.432Z
5fc0bcb41160c47d1d43856b
https://cdn-avatars.huggingface.co/v1/production/uploads/5fc0bcb41160c47d1d43856b/AHCEW4TfTdyjNBx-V_F5A.png
Damien Sileo
sileod
user
sileod
MortalMATH: Evaluating the Conflict Between Reasoning Objectives and Emergency Contexts
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.
Specialized reasoning models prioritize task completion over safety, potentially ignoring critical emergencies during complex calculations.
large language models deep reasoning safety mortality computational time task completion