Co-Occurrence Priors Override Visual Spatial Evidence: Understanding False Alarms in Vision-Language Models for Construction Struck-By Hazard Detection

Research demonstrates that vision-language models (VLMs) produce false alarms in construction safety hazard detection because they rely on learned co-occurrence patterns (e. g.

Research demonstrates that vision-language models (VLMs) produce false alarms in construction safety hazard detection because they rely on learned co-occurrence patterns (e.g., hard hats near people) rather than actual spatial evidence of struck-by hazards. The study reveals a critical failure mode in VLM reasoning that has direct implications for deploying these models in safety-critical enterprise applications across construction, healthcare, and insurance sectors.

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