arXiv Machine Learning June 2, 2026 By Bumjun Kim, Dongjae Jeon, Moongyu Jeon, Albert No

DAPD: Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs

Read the original on arXiv Machine Learning →

arXiv:2603. 12996v2 Announce Type: replace Abstract: Parallel decoding for Diffusion LLMs (dLLMs) is difficult because each denoising step provides only token-wise marginal distributions, while unmasking multiple tokens simultaneously requires accounting for inter-token dependencies.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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