Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference
Diffusion large language models (dLLMs) offer a promising alternative to autoregressive generation by decoding multiple tokens in parallel through iterative denoising. However, increasing decoding parallelism often degrades generation quality, as early errors can contaminate later contexts. Revocable decoding mitigates this issue by re-evaluating decoded tokens and remasking unreliable ones, but existing methods overlook that unreliable tokens may also corrupt the verification context itself. We identify this failure mode and propose Dependency-Aware Revocable Decoding (DARD), a training-free framework that separates tokens into masked, candidate, and unmasked states. DARD verifies candid...
| Authors | Wooje Park, Insu Lee, Minyoung Noh et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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