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A new atomic evaluation framework, WM-ABench, systematically assesses Vision-Language Models' capabilities as internal world models, revealing significant limitations in their spatial, temporal, and dynamic scene understanding, as well as their ability to perform causal, transitive, and compositional predictions, falling substantially short of human performance.