arXiv:2606.02443v1 Announce Type: new Abstract: Between the first visible sign of danger and the moment an accident occurs, there is often a window where intervention remains possible. Video-capable multimodal large language models (MLLMs) could serve as always-on safety monitors that issue warnings during this window. Yet current benchmarks do not test this ability: they rely on static inputs, ignore timing precision, and omit false-positive measurement on safe scenes. We present PaSBench-Video, a 740-video benchmark with 481 risk and 259 no-risk videos across four domains: driving, healthcar
The rapid advancement in multimodal large language models (MLLMs) and increasing demand for real-time AI applications facilitate the timing of this new benchmark.
This benchmark addresses a critical gap in evaluating AI's ability to provide proactive safety warnings in dynamic environments, moving beyond static analysis to real-time risk assessment.
The development of MLLMs for real-time proactive safety will be significantly advanced by a standardized benchmark that includes timing precision and false-positive measurement, leading to more reliable AI safety monitors.
- · AI safety researchers
- · Video-capable MLLM developers
- · Automotive industry
- · Healthcare sector
- · Developers of static-input MLLMs
- · Industries reliant solely on reactive safety systems
Improved MLLMs will emerge that are specifically trained and validated for proactive safety applications.
The widespread deployment of MLLM-powered safety monitors will lead to a measurable reduction in accidents and injuries across various domains.
New regulatory frameworks and certification standards will develop to govern the development and deployment of proactive AI safety systems.
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