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LLaVA-OV-2's codec-stream tokenization lets it crush existing video-language models, especially in tasks requiring fine-grained temporal understanding of high-frequency motion.
Unified multimodal models often *hurt* performance on multimodal understanding tasks, except for spatial reasoning, visual illusions, and multi-round reasoning, challenging the assumption that generation universally improves understanding.
Turns out, skipping the boring parts of a video (like static backgrounds) makes your vision AI both faster and smarter, beating state-of-the-art models with less data.
