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State-of-the-art image-to-video models are alarmingly vulnerable to visual prompt attacks, achieving up to 100% success in triggering harmful outputs.
Advanced RS MLLMs struggle with negation, but a novel learning method can dramatically enhance their understanding using minimal unlabeled data.
PULSE slashes communication overhead by 89% and boosts training throughput by up to 2.3x, revolutionizing how we scale diffusion models across GPU clusters.
PACT reduces safety violations by over 30% while simultaneously boosting task success, reshaping the landscape of safe robotic manipulation.
