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A unified architecture achieves SOTA embodied AI by jointly learning physical reasoning, world generation, and action prediction, outperforming prior models in generalization and long-horizon tasks.
By integrating semantic foresight from vision-language models, SG-WAM ensures that robotic actions are not just visually accurate but also linguistically aligned, transforming how robots interpret and execute tasks.