EventWorth: Risk-Controlled Event–Frame Prediction with Delayed Visual Anchors

Conventional cameras often struggle under fast motion or challenging illumination, whereas event cameras capture fine-grained temporal changes that can complement frame observations for temporally causal video prediction. However, event quality varies across sensing conditions, and corrupted or unreliable events can degrade prediction rather than improve it. Existing event–frame methods mainly focus on exploiting events, but rarely determine whether they should be used for each prediction relative to a deployable frame-only fallback. This leaves negative-transfer risk largely uncontrolled. To address this problem, we propose EventWorth, a selector-agnostic framework for risk-controlled event use. EventWorth learns baseline-relative gain and harm scores from paired supervision provided by delayed future anchors. Validation data propose and freeze a selective policy, which is independently certified before deployment. Event assistance is enabled only after certification; otherwise, the system falls back to the frame-only branch. Under the stated round-level i.i.d. certificate assumption, EventWorth provides finite-sample control over the probability of deploying a selector whose selected harm risk exceeds a prescribed target. Experiments show that the analytic EventWorth policy is certified in all five seeds while retaining 50.44% development-test coverage, 1.30% harm, and 0.0220 system gain. These results show that selective event use can retain useful event information while controlling deployment risk.