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Current state-of-the-art world models struggle to maintain spatial consistency and reliable state evolution over long-horizon interactions, as revealed by the new PlayWorld benchmark.
AffordanceVLA transforms robotic manipulation by using structured affordance cues to create precise perception-action mappings, outperforming traditional models.
