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The refined proposal enables a sparse encoding that magnifies evidence and excludes background tokens, and is called Vision-RL2, which improves accuracy over the base model at every token budget and surpasses its largest-budget accuracy with about 4 times fewer visual tokens.
Stream4D transforms video generation by enabling coherent motion and dynamic scene representation, outperforming static critics that hinder realism.
MLLMs can achieve 4x faster inference without sacrificing accuracy by intelligently focusing on only the image regions relevant to the query.
