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A corruption-lattice cost predicts the minimum steps for generative decoding

huggingface.co by T. Y. Tsui et al. Editor score 7/10

The paper models decoding schedules as paths on a corruption lattice and defines their cost by discarded dependence; for Markov data on a graph, zero-cost schedules require steps equal to the graph's treedepth. This offers a way to assess decoding schedules before running them.

What

The paper represents diffusion, autoregression and hybrid generative models as paths on a corruption lattice, with schedule cost measuring dependence discarded by parallel steps.

Method

For zero-cost schedules, the minimum steps follow data geometry; for Markov data on a graph, the minimum equals the graph's treedepth.

Why it matters

The cost can rank decoding schedules before generation, helping engineers reason about step budgets for text, image and video models.

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