PACE-dLLM: Elastic Block Decoding via Confidence Cliff Estimation for Diffusion Language Models

Diffusion large language models (dLLMs) such as LLaDA and Dream now rival autoregressive LLMs in quality while retaining native parallel decoding. To deploy them efficiently, block-wise decoding partitions generation into blocks of size $B$ and commits a fraction of each block before moving on, but $B$ conflates two roles: \emph{how far the model can look ahead} and \emph{how many tokens get committed per step}. Recent accelerators relax this trade-off with indirect heuristics, yet the underlying difficulty is already exposed at every forward pass by the model's per-step confidence. Specifically, the in-window confidence profile exhibits a context-dependent \emph{cliff} (a high plateau, a sigmoidal transition, and a residual floor) that directly encodes how far ahead is safe to look. We propose \textbf{PACE-dLLM}, a decoder that handles the two roles independently. At each step, PACE-dLLM fits the cliff in closed form on the current window's confidences and reads off the next prediction horizon at the cliff's saturation point; a separate confidence threshold decides which tokens are committed. Both rules read the same signal, add a single hyperparameter, and admit a strict-dominance guarantee over fixed-block decoding. Extensive experiments across four reasoning and code benchmarks on two open-source dLLM backbones demonstrate that PACE-dLLM achieves the best average accuracy at a roughly $\mathbf{4.5\times}$ wall-clock speedup over the unaccelerated semi-AR baseline, pushing the Pareto frontier outward.