EventTriggerBench: Discovering Plausible Trigger Events for Explanatory Video Understanding

Understanding videos requires more than recognizing what happens; it also requires identifying which earlier events make a later event plausible. We introduce EventTriggerBench, a benchmark for target-conditioned trigger attribution in videos. Given an ordered sequence of localized events and a target event that may appear anywhere in the timeline, models must identify earlier events that provide plausible explanatory support for the target. EventTriggerBench is designed to test a key gap in current video understanding: models often capture temporal co-occurrence, but struggle to distinguish events that merely precede a target from those that meaningfully explain it. To study this gap, we provide auto-verified trigger labels and evaluate a broad range of heuristic, sequential, graph-based, multimodal, causal, and LLM baselines. We further introduce EventGraph-Trigger, a multimodal event-graph reasoning framework for target-aware trigger attribution. Our experiments show that trigger attribution remains challenging even for strong video-language and LLM-based models, especially when explanations require long-range context, multi-step event chains, or distinguishing direct triggers from incidental earlier events. These findings suggest that target-conditioned trigger discovery offers a useful diagnostic lens for moving video understanding beyond event recognition toward structured explanatory reasoning.