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Autonomous driving VLAs can be made conflict-aware by dreaming world models that strictly follow navigation signals, revealing unsafe instruction scenarios.
PALATE enables personalized portrait retouching at a fraction of the cost, requiring only 512 bytes of user-specific data while achieving superior preference prediction accuracy.