GeoBlock: Inferring Block Granularity from Dependency Geometry in Diffusion Language Models
Block diffusion enables efficient parallel refinement in diffusion language models, but its decoding behavior depends critically on block size. Existing block-sizing strategies rely on fixed rules or heuristic signals and do not account for the dependency geometry that determines which tokens can be safely refined together. This motivates a geometry view of diffusion decoding: \emph{regions with strong causal ordering require sequential updates, whereas semantically cohesive regions admit parallel refinement.} We introduce GeoBlock, a geometry-aware block inference framework that determines block granularity directly from attention-derived dependency geometry. Instead of relying on predef...
| Authors | Lipeng Wan, Junjie Ma, Jianhui Gu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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