Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment

Abstract: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 temperature 0.0, we find that all tested models consistently produced no precautionary warning signal across the tested scenarios where multiple sensors are simultaneously elevated below their individual safety limits, while achieving near-perfect accuracy on single-sensor threshold violations. All five models (ChatGPT-4o, Gemini 2.5 Flash, DeepSeek, Kimi, Llama 3.1 8B) score near zero on Category A multi-sensor scenarios (Q2: 0.000-0.208; Q3: 0.000-0.592) compared to strong performance on single-sensor scenarios (Category B Q1: 0.975-1.000). Structured tabular formatting shows no consistent advantage over plain prose; ChatGPT-4o performs significantly better under prose (p = 0.001). These findings have direct implications for practitioners deploying the tested models in physical safety monitoring systems.

14 pages, 6 figures. Benchmark dataset, evaluation code, and raw results publicly available at: this https URL

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2607.20476 [cs.AI]

(or arXiv:2607.20476v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.20476 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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