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Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch tokens. However, visual information is unevenly distributed: some regions require fine-grained detail, while others admit compact representations. Down...
VideoMM is introduced, which marks a paradigm shift from model-centric downsizing to adaptive perceptual granularity and accelerates inference by 2.73 times over current leading methods, establishing a highly scalable paradigm for long-video understanding.
LibraSpec, a training-free and plug-and-play algorithm that iteratively determines the speculative length using drafter confidence scores, is developed and it is proved that LibraSpec monotonically converges toward the optimal speculative length.
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