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Compact, automatically selected anatomical regions can drastically reduce hallucinations in medical VLMs without requiring expert annotations.
A novel data synthesis approach enables a lightweight model to outperform state-of-the-art planning systems by over 10x in household appliance manipulation tasks.
Transformers generalize out-of-distribution not by clever interpolation, but by learning a separate, orthogonal representation subspace for unseen tasks.
