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Learning insertion order in sequence generation can drastically improve modeling quality and generalization, challenging the dominance of fixed-canvas methods.
By mapping permutations to a continuous space of "soft ranks," this new diffusion approach makes learning permutation distributions far more tractable, especially for long sequences.
