AI RESEARCH
SemBlock: Semantic Boundary Dynamic Blocks for Diffusion LLMs
arXiv CS.CL
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ArXi:2606.04964v1 Announce Type: new Diffusion language models (DLMs) generate text through iterative denoising, and blockwise decoding improves their practicality by committing tokens in local blocks. However, existing blockwise methods typically rely on fixed block sizes or delimiter-based runtime signals, which do not necessarily align with semantic boundaries. In this paper, we propose SemBlock, a semantic-boundary-driven dynamic block decoding framework for diffusion LLMs.