Masked Diffusion Parallel Decoding Mapped to Random Sequential Adsorption
The physics framing turns schedule design for masked diffusion decoding into a geometry problem, giving practitioners a principled way to choose unmasking steps instead of heuristics. Builders working on parallel decoding, such as teams tuning LLaDA- or Mercury-style diffusion LMs, can use the closed-form metric to cut error at a fixed step budget. The finding that errors concentrate early for long-correlation text directly informs how to allocate compute across generation steps.