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Nanyang Technological University
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Achieve 25x speedup in World Action Models with <1% performance drop by enabling one-step action generation and asynchronous expert execution.
RAPID is proposed, a self-referenced latent maximization framework that removes external dependencies and encourages the same model update to induce consistently disruptive effects across training samples, thereby improving reachability and establishing a paradigm for real-time protection against unauthorized distillation in deployed T2I systems.
