Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation
Diffusion-based large language models (dLLMs) support parallel text generation via iterative denoising, yet inference remains latency-heavy because many steps are spent on redundant refinement and repeated remasking of tokens whose final values are already determined. Prior acceleration methods mainly depend on step-local confidence heuristics or fixed schedules, which are sensitive to prompt and task variation and ignore strong positional effects within a sequence. We cast diffusion decoding as a dynamic control problem and show that token-wise denoising trajectories provide the key signal for reliable control. We propose a trace-aware decoding framework with two components. First, Tempo...
| Authors | Zekai Li, Ji Liu, Yiqing Huang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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