Splitting masked-diffusion decoding into five axes, researchers find state adaptation pays off only in a few predictable states, and selective intervention lifts task-macro utility by 3.2, 4.0, and 5.6 points across three models
The work factorizes masked diffusion language model (MDM) inference into five axes—score, cardinality, region, commitment, and planning—defines adaptation opportunity as the one-step utility advantage of the best candidate action over a validation-selected fixed action, finds across three models (LLaDA-8B-Instruct, LLaDA-1.5, Dream-7B) and ten tasks that adaptation opportunities are highly heterogeneous, and proposes selective adaptation in which lightweight detectors calibrated on validation prompts identify high-opportunity states, capturing for example 56.9% of the candidate-set oracle opportunity by adapting only the top 10% of states on LLaDA-8B constrained JSON filling, with end-to-end selective decoding raising task-macro utility from 0.392 to 0.424 (LLaDA-8B), 0.344 to 0.