AI RESEARCH
Attention-Based Sampler for Diffusion Language Models
arXiv CS.LG
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ArXi:2604.08564v1 Announce Type: cross Auto-regressive models (ARMs) have established a dominant paradigm in language modeling. However, their strictly sequential decoding paradigm imposes fundamental constraints on both inference efficiency and modeling flexibility. To address these limitations, diffusion-based large language models (dLLMs) have been proposed, offering the potential for parallel decoding and flexible language modeling.