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This paper proposes a unified optimal transport framework for cold-start active learning, introducing a Sinkhorn-based algorithm (ε-AS) that adapts regularization strength to data and achieves state-of-the-art results on six datasets, including improving ImageNet-1k accuracy by 1.29% over prior methods while reducing selection time by 56.2%.