DAPD: Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs
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. We propose Dependency-Aware Parallel Decoding (DAPD), a simple, training-free decoding method that uses self-attention to induce a conditional dependency graph over masked tokens. At each iteration, edges in this graph capture strong token interactions, while non-edges indicate weak dependence. Parallel decoding is then reduced to selecting an independent set on the graph and unmasking the selected tokens in parallel. This avoids co-updating strongly coupled to...
| Authors | Bumjun Kim, Dongjae Jeon, Moongyu Jeon et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 March 2026 |
| Last updated at source | 1 June 2026 |
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