10 papers
Region-Adaptive Sampling for Diffusion Transformers
Ziming Liu, Yifan Yang, Chengruidong Zhang +4
Diffusion models (DMs) have become the leading choice for generative tasks across diverse domains. However, their reliance on multiple sequential forward passes significantly limit…
Time-Annealed Perturbation Sampling: Diverse Generation for Diffusion Language Models
Jingxuan Wu, Zhenglin Wan, Xingrui Yu +4
Diffusion language models (Diffusion-LMs) introduce an explicit temporal dimension into text generation, yet how this structure can be leveraged to control generation diversity for…
MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE
Geng Zhang, Yuxuan Han, Yuxuan Lou +3
Mixture-of-Experts (MoE) enables efficient scaling of large language models by activating only a subset of experts per input token. However, deploying MoE-based models incurs signi…
HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models
Xin Yan, Zhenglin Wan, Feiyang Ye +4
Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment on resource-constrained robots an…
K-Sort Eval: Efficient Preference Evaluation for Visual Generation via Corrected VLM-as-a-Judge
Zhikai Li, Jiatong Li, Xuewen Liu +7
The rapid development of visual generative models raises the need for more scalable and human-aligned evaluation methods. While the crowdsourced Arena platforms offer human prefere…
DiffuSpeech: Silent Thought, Spoken Answer via Unified Speech-Text Diffusion
Yuxuan Lou, Ziming Wu, Yaochen Wang +6
Current speech language models generate responses directly without explicit reasoning, leading to errors that cannot be corrected once audio is produced. We introduce \textbf{``Sil…