Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment
Large Language Models fail to detect multi-sensor physical hazards when individual sensor readings are below safety limits, despite near-perfect performance on single-sensor threshold violations. Five major models (ChatGPT-4o, Gemini 2.5 Flash, DeepSeek, Kimi, Llama 3.1 8B) scored near zero on multi-sensor joint assessment scenarios, indicating a critical gap in reasoning about cumulative risks. Structured tabular formatting provided no consistent advantage over plain prose for hazard assessment
Analysis
TL;DR
- Large Language Models fail to detect multi-sensor physical hazards when individual sensor readings are below safety limits, despite near-perfect performance on single-sensor threshold violations.
- Five major models (ChatGPT-4o, Gemini 2.5 Flash, DeepSeek, Kimi, Llama 3.1 8B) scored near zero on multi-sensor joint assessment scenarios, indicating a critical gap in reasoning about cumulative risks.
- Structured tabular formatting provided no consistent advantage over plain prose for hazard assessment, with ChatGPT-4o performing significantly better under prose conditions.
Why It Matters
This research highlights a dangerous blind spot in deploying LLMs for industrial safety and physical monitoring systems, where cumulative low-level risks often precede catastrophic failures. Practitioners relying on these models for automated hazard detection must recognize that current architectures struggle with correlating multiple sub-threshold signals, potentially leading to missed warnings. The findings necessitate caution and additional validation layers when integrating LLMs into critical infrastructure safety protocols.
Technical Details
- Benchmark Scope: Evaluated five LLMs (ChatGPT-4o, Gemini 2.5 Flash, DeepSeek, Kimi, Llama 3.1 8B) across 60 scenarios involving multi-sensor joint assessment, response proportionality, and pattern disambiguation.
- Methodology: Conducted 1,800 API calls at temperature 0.0 to ensure deterministic outputs, comparing model responses against ground-truth hazard assessments.
- Performance Metrics: Models achieved high accuracy (Q1: 0.975-1.000) on single-sensor threshold violations but failed completely on multi-sensor scenarios (Q2: 0.000-0.208; Q3: 0.000-0.592), consistently missing precautionary warnings for simultaneous sub-limit elevations.
- Input Format Analysis: Tested structured tabular vs. plain prose inputs; found no general advantage for tables, though ChatGPT-4o showed statistically significant improvement with prose (p = 0.001).
Industry Insight
- Safety-Critical Deployment Risks: Organizations should not rely solely on LLMs for real-time physical hazard monitoring without robust fallback mechanisms or hybrid models that incorporate explicit rule-based logic for multi-variable correlations.
- Prompt Engineering Limitations: The failure of structured data formats suggests that simply formatting sensor data as tables does not mitigate the underlying reasoning deficits in current LLMs for this specific task.
- Need for Specialized Fine-Tuning: There is a clear opportunity for developing domain-specific fine-tuned models or reinforcement learning approaches focused on multi-sensor correlation to address this identified capability gap.
Disclaimer: The above content is generated by AI and is for reference only.