Masked diffusion language models (MDLMs) enable efficient parallel decoding but are limited by a monotonic unmasking policy, where committed tokens cannot be revised. While remasking-based methods mitigate early errors, they mainly intervene during generation. In this work, we study post-hoc refinement of a completed draft and find that naive correction often fails because of contextual lock-in, a phenomenon in which local error patterns become self-reinforcing. To address this, we propose PURE (Post-hoc Unlocking and REfinement), a training-free inference algorithm for two-phase decoding. PURE profiles confidence dynamics during drafting to identify unstable regions via an instability score (𝛥_i), then unlocks them through deterministic window masking and stochastic leftward relaxation. On reasoning benchmarks, PURE substantially improves accuracy when applied to LLaDA-8B-Instruct, including a gain of +12.9 points over the baseline on GSM8K. These gains require only a small refinement budget, yielding a favorable compute-quality trade-off for discrete diffusion decoding.

Standard semi-autoregressive decoding for full-attention masked diffusion language models (MDLMs) computes predictions for future blocks but discards them until those blocks become active. When introduced as soft-future context, these predictions reveal a stability contrast: hypotheses later revised during decoding exhibit larger distributional shifts than those eventually retained. We introduce FUSA (Future-aware Semi-Autoregressive Decoding), a training-free decoder that exploits this contrast by alternating the next block between masked and soft representations and committing active-block tokens that remain stable across the two views, without additional model forward calls. On LLaDA-8B-Instruct, FUSA improves scores across reasoning and code-generation benchmarks while delivering 2.10– 3.94× wall-clock speedups over Top-1 decoding, with gains of up to 5.1 points. Additional results show that FUSA transfers to other MDLMs and remains effective under KV-cached inference and in long-form instruction following. Our code is available at https://github.com/yangryeol/FUSA.