arXiv: cs.AI preprintsInternational6 November 2026
Masked diffusion as random sequential adsorption: an exactly solvable model for parallel decoding
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arXiv:2611.07845v1 Announce Type: cross Abstract: Masked diffusion models generate text by reversing a stochastic masking process. Unlike autoregressive LLMs, which generate text one token at a time, they can reveal many tokens in parallel, at the price of an error, since tokens revealed together are drawn independently. We show that this trade-off maps onto the statistical physics of driven many-particle systems. (i) For any data, the error of parallel generation is the entropy production of the generation process relative to the reversed masking process. (ii) For Markov data, generation is a
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