Seeing the Scene Was Easier Than Predicting Its Next State

SpaceCast-Bench moves beyond reading visible spatial relations to an observe-transform-infer sequence. Its 3,862 questions from 182 real scenes span sixteen task types covering perception, local prediction and global prediction. Among twenty-one tested models, the strongest reached 58%, against 87.2% human performance; specialized spatial models remained near chance. Controlled analyses found bridge views useful for combining observations and explicit 3D evidence more consistently helpful than generated outcome images or video. Fine-tuning Qwen3-VL-4B on programmatically generated data raised its benchmark score from 34% to 65.7%, alongside gains across six outside benchmarks. The results separate naming a present relation from updating a scene that can no longer be directly seen.