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Z-Reward achieves 41.3% better human preference alignment in text-to-image generation by transforming complex reasoning into efficient score distributions.
Achieve up to 2.5X faster video object removal by focusing DiT computations only on the essential tokens dictated by the mask.
Current image quality metrics struggle to articulate *why* one high-quality image is better than another, but this challenge shows MLLMs are closing the gap by providing expert-level explanations.
