PaSBench-Video: A Streaming Video Benchmark for Proactive Safety Warning

ArXi:2606.02443v1 Announce Type: cross 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.