Explore, Revisit, or Commit: Prediction Dynamics in Continuous Diffusion Language Models

Continuous diffusion language models (CDLMs) refine continuous states without committing to tokens, so although every sampling step costs a full model evaluation, what each step contributes remains unclear. We address this question by studying prediction dynamics, how CDLM updates form and revise token predictions, through a framework that assigns each position at each step to commitment, revisitation, or exploration. Applying it to four representative CDLMs (LangFlow, Plaid, ELF, and Cola-DLM), we find that revisitation, in which the predicted distribution moves away from an earlier state and then back toward it, is widespread: it persists after the top-1 token stabilizes, and additional sampling steps advance commitment without removing it. We hypothesize that revisitation stems from candidate multiplicity and cross-position dependence, and our experiments support both sources: revisitation often involves competition among a few candidates, with the same rival returning at the same position, and in LangFlow and ELF, freezing the prediction memory of other positions changes how a revisiting position is revised. Building on these findings, we propose velocity blending, a training-free sampling control that blends consecutive velocities in two ways: late interval merging saves about a quarter of sampling time while changing far fewer native tokens than early merging, whereas velocity damping has setting-dependent effects on revisitation and quality. Taken together, our results separate observing, altering, and exploiting prediction dynamics.