World models can learn to calibrate themselves to scene-specific physics by encoding exploratory interaction data into reusable descriptor tokens, improving prediction accuracy without requiring physics labels or fine-tuning.
PhysiCalWorld improves world models by learning to identify and adapt to the unique physics properties of a scene (like object mass or friction) through brief exploratory interactions. Instead of fine-tuning or using physics equations, it encodes these properties into a compact token that guides predictions, making visual planning more accurate in novel environments.