Title: Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding
Primary area: generative models
Keywords: diffusion language models, compute efficient sampling, skipping compute, adaptive attention

Abstract: Masked Diffusion Language Models generate sequences via iterative sampling that progressively unmasks tokens. However, they still recompute the attention and feed-forward blocks for every token position at every step---even when many unmasked tokens are essentially fixed, resulting in substantial waste in compute.
We propose \textbf{\textsc{SureLock}}: when the posterior at an unmasked position has stabilized across steps (our \emph{sure} condition), we \emph{lock} that position---thereafter skipping its query projection and feed-forward sublayers---while caching its attention keys and values so other positions can continue to attend to it.
This reduces the dominant per-iteration computational cost from $O(N^2d)$ to $O(MNd)$ where $N$ is the sequence length, $M$ is the number of unlocked token positions, and $d$ is the model dimension.
In practice, $M$ decreases as the iteration progresses, yielding substantial savings.
On LLaDA-8B, SureLock reduces algorithmic FLOPs by 30--50\% relative to the same sampler without locking, 
while maintaining comparable generation quality.
We also provide a theoretical analysis to justify the design rationale of SureLock:  monitoring only the local KL at the lock step suffices to bound the deviation in final token probabilities.