Heimdall: Hindsight Distillation for Proactive Warning in Streaming Video

Streaming vision-language models can judge whether a scene is dangerous. Proactive warning asks them to say so before the accident, within a preset cap on false alarms, and on the device beside the camera. However, training such a warner requires supervision, and the annotated accident times that anticipation methods train on are expensive to obtain and absent for a newly deployed camera's footage. We observe that a frozen large verifier is uncertain at the moment a warning is due but confident a few seconds later, once the accident is in view. This later verdict can become a training target for the earlier moment without any annotation. We propose Heimdall, which queries a frozen 8B verifier a few seconds after each window of the training stream, converts those later verdicts into a multi-horizon risk target for the window itself, and distills on that target a 2B model that runs in real time on a Jetson. At deployment the two models run as one online policy. Each alarms when its risk score crosses its threshold, and the two thresholds are chosen together so that the policy as a whole stays within the cap. On the held-out test split of PaSBench-Video, Heimdall warns before the accident on 1.5 times as many clips as a zero-shot model with the same verifier at half its false-alarm rate, and on a third more than a model distilled from the same verifier at the same moment. Published warners that find more accidents raise two to four times as many false alarms as the cap allows.