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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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T03:34:47.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.