Research
Masked diffusion as random sequential adsorption: an exactly solvable model for parallel decoding
This paper presents a theoretical framework linking masked diffusion models for text generation to statistical physics of driven many-particle systems. It shows that the error in parallel decoding corresponds to entropy production, and for Markov data, the generation process maps to random sequential adsorption with labelled gaps, solved exactly. The authors derive a thermodynamic metric from mutual information, showing optimal schedules are geodesics, and that longer text correlations concentrate error at the start of generation.
masked diffusion modelsparallel text generationstatistical physics of machine learning