Beyond Plausible Motion: Benchmarking and Improving Ordered Event-Transition Generation in Few-Step Autoregressive Video Models

Few-step autoregressive (AR) video models enable low-latency streaming generation by predicting future frame chunks conditioned on previously generated content, but this chunk-wise formulation makes video temporal transition ambiguous. Existing metrics evaluate perceptual quality, multi-event completion, but they do not explicitly resolve realization of a single short-horizon event transition. To bridge this gap, we introduce **OETGBench**, an **O**rdered **E**vent-**T**ransition **G**eneration **Bench**mark designed to evaluate whether a short-horizon T2V generation visibly progresses from an initial state to a target outcome in the intended temporal order. OETGBench contains 2,500 curated tasks spanning four broad event-transition families, with each task decomposed into ordered onset, progression, and outcome phases to assess short-video models' ability to complete requested events in the correct temporal order. Alongside OETGBench, we propose *OETGEval*, a hierarchical evaluation framework combining video-level diagnostics with window-level phase verification of onset, progression, and outcome in their intended temporal order. Finally, we demonstrate that these evaluation signals are actionable through *Anchor-Guarded Phase-Potential Post-Training* (AG-P3T), a reward-guided LoRA method that converts OETG evidence into anchor-relative rewards and permits positive updates only when ordered event completion improves without violating quality or shortcut guardrails. Experiments reveal that our methods ensure alignment with human evaluations and reveal distinct failure profiles among generators with similar event scores. AG-P3T improves the Event Transition Score while maintaining comparable VBench performance, demonstrating the utility of OETGEval feedback for post-training.