Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment
LLMs struggle to identify aggregate safety risks despite high accuracy in single-sensor threshold detection.
We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data. Testing 60 scenarios across three categories - multi-sensor joint assessment, response proportionality, and pattern disambiguation - with 1,800 API calls at temper...
ArXiv CS.AI
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Current large language models fail to synthesize multi-sensor data, showing a dangerous inability to recognize hazards when individual readings remain below threshold limits. This research highlights a critical reliability gap that must be addressed before deploying LLMs in real-world physical safety and monitoring infrastructure.
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