WorldBench: Selective State Maintenance in Multi-Shot Video Generation

Recent advances in video generation have enabled increasingly coherent multi-shot video generation with controllable camera viewpoints and recurring subjects. While recent multi-shot video generators have made substantial progress in cross-shot visual consistency, they still frequently exhibit world-state collapse, leading to inconsistencies in subject count and spatial assignments across shots. Motivated by this gap, we introduce WorldBench, a controlled benchmark for evaluating world-state maintenance in multi-shot video generation. Each episode establishes an initial world state with a wide shot and later re-observes the resulting world from a different viewpoint after controlled narrative changes. Experiments on representative open- and closed-weight models show that even Seedance 2.5 achieves only 56.2% end-to-end success, confirming that world-state maintenance remains a substantial challenge and highlighting the significance of WorldBench for systematically evaluating this capability. Our code is available at https://anonymous.4open.science/r/WorldBench-1.