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Measured Coupling Improves Parallel Decoding in Diffusion Language Models

This paper introduces a method to improve parallel decoding in diffusion language models by directly measuring the coupling between token positions—how committing one token changes confidence at another—using cheap probes inside the main forward pass. This replaces the mean-field decoding proxy that penalizes similar predictions, requiring no training or extra model. On three models and four benchmarks, the method reduces decoding steps and is faster in most settings, including with a key-value cache, at an average accuracy cost of 1.1 points.