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Standard visual token pruning targets the wrong encoder depth: tapping into intermediate representations while asymmetrically skipping decoder sub-layers cuts MLLM inference FLOPs by 79% without requiring any retraining.
Achieving precise concept removal in text-to-video models without sacrificing generation quality could redefine safety standards in generative AI.
