Contextual Commitment for Parallel Masked Diffusion Decoding

Masked diffusion decoders can use unresolved predictions as context before committing them to discrete tokens. Choosing which predictions to commit and how strongly to update this provisional context requires accounting for dependence between the two groups. Yet the model provides only per-position distributions, which do not determine their joint dependence. To address this problem, we propose Contextual Commitment (CoCom). Under one dependence budget, the selected commitments constrain how strongly pending predictions can update the next input. We couple these decisions by bounding total correlation in a reference observation of committed variables and randomly retained pending variables. The bound uses only current marginals and holds for every compatible joint distribution. Both decisions therefore reuse the current model predictions, with zero additional forward passes, no additional training, and no separately tuned update strength. Across four backbones and four benchmarks, CoCom improves the accuracy–computation tradeoff over the compared accelerated decoders. Budgets selected on LLaDA-1.5 transfer directly to LLaDA-Ins and Dream, where CoCom achieves the highest accuracy among accelerated decoders in seven of eight settings. Ablations support the benefit of selecting commitments and pending updates jointly.