SpaceTimeBench: Evaluating Space And Time In The World Model

Generative world models aim to serve as universal physical simulators, yet existing benchmarks fail to verify the fundamental prerequisite of Newtonian Law: that space and time must remain absolute and mathematically consistent across different observer perspectives. To bridge this evaluation gap, we introduce SpaceTimeBench, a novel Video-to-Video framework that utilizes prompt-injected geometric anchors alongside DePT3R and SAM 2 to extract strictly normalized 4D coordinates, testing whether models perfectly conserve spatial distances and temporal durations across varied camera views. Extensive evaluations across 17 leading architectures reveal a severe capability deficit quantified by our Final Spacetime Yield (FSY), demonstrating that even state-of-the-art models peak at a mere 65.0% success rate. Crucially, SpaceTimeBench functions as an evaluation oracle to expose a False Discovery Rate (FDR) of up to 42.9% regarding spurious compliance in existing benchmarks, proving that evaluating Newton's First Law without verifying underlying spatiotemporal prerequisites severely inflates scores and masks the reality that current architectures remain mere two-dimensional pattern matchers.