DecoCal: Decoding with Calibration in Diffusion Large Language Models

Diffusion Large Language Models (DLLMs) generate text via iterative masked-token denoising, supporting parallel prediction and bidirectional context modeling. Despite these advantages, decoding remains challenging: many tokens appear predictable early, yet single-step predictions are often unstable, exhibiting temporal oscillations or overconfidence, making it difficult to determine which tokens can be safely committed. To address these challenges, we propose DecoCal 1 , a Decoding framework that explicitly performs Calibration of tokenlevel confidence across diffusion steps and leverages the calibrated results to guide decoding decisions. Specifically, DecoCal aggregates historical predictions to maintain calibrated confidence, triggering unmasking only when a token is sufficiently stable, while a remasking mechanism allows revision of premature commitments. This calibration-based design enables early decoding of reliably converged tokens while deferring or correcting unstable ones, balancing reliability and speed. Experiments on multiple DLLMs and benchmarks show that DecoCal improves generation accuracy compared to existing strategies. Our results highlight the importance of temporal calibration in unlocking the full potential of diffusion-based language generation. 𝐩 ! (#) 𝐪 !%& (#) 𝐪 ! (#) 𝑥 (') [MASK] 𝑥 (() [MASK] 𝐾𝐿 a. Confidence Check b. Consistency Check Calibration Sequence [MASK] 𝑥 (') 𝑥 (&) 𝑥 (() 𝑥 ()) [MASK] Unmask Remask 𝑥 (') [MASK] 𝑥 (() [MASK] [MASK] 𝑥 (') 𝑥 (&) [MASK] 𝑥 ()) [MASK] Cool-down Delayed Consistency Check