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VIF-Bench is introduced, a benchmark of 1,241 tasks designed to assess the edge of model capabilities in this joint setting in which multiple references must be composed under multiple and heterogeneous visual-instruction images, and uncover three findings.
This work proposes AutoRef, which optimizes the harness automatically while keeping both models frozen: a coding agent iteratively rewrites the harness code, and discovers AutoRef-Harness, which improves the open-weight FLUX.
This work formalizes target-mode coverage as target-mode coverage, the coverage of a predefined set of semantically specified modes, and proposes multi-axis max@K, a group-based reinforcement learning objective for improving it in diffusion-based T2I models.
The ablation study shows that when using SSMs for temporal modeling, incorporating bidirectionality and selective scans enhances video generation performance, and SSM-based models incur lower computational cost to achieve the same Fréchet Video Distance as attention-based models.
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