3 papers
cs.AI2026
One Latent, Many Tokens: Jointly Learning Compressed Embeddings for Efficient Language Diffusion
Yulin Yuan, Ying Zhang, Xiangming Meng
Most continuous diffusion language models process one latent position per token at each sampling step, making generation expensive. Two-stage methods lower the cost by reducing the…
cs.LG2026
Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models
Yulin Yuan, Hongshuo Zhao, Xiangming Meng
Diffusion-based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel. This turns each decoding step into a pos…
cs.LG2026
Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models
Heqiang Qi, Wei Huang, Mingyuan Bai +1
Masked diffusion language models (MDLMs) enable parallel decoding by predicting all masked positions at each denoising step, yet existing training-free samplers usually decide whic…