6 papers · 1 filter
DivMoE: Fine-Grained MoE Upcycling via Cross-Domain Expert Composition
Yuxuan Lou, Kai Yang, Geng Zhang +2
Mixture-of-Experts (MoE) architectures have become essential for scaling large language models, with recent work demonstrating the benefits of fine-grained expert designs. Training…
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…
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…
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…
MoST: Mixing Speech and Text with Modality-Aware Mixture of Experts
Yuxuan Lou, Kai Yang, Yang You
We present MoST (Mixture of Speech and Text), a novel multimodal large language model that seamlessly integrates speech and text processing through our proposed Modality-Aware Mixt…
Denoising Time Matters:Diverse Generation in Diffusion Language Models
Jingxuan Wu, Zhenglin Wan, Xingrui Yu +5
Diffusion language models (Diffusion-LMs) generate text through iterative denoising, exposing a temporal structure that is largely absent from autoregressive decoding. In this pape…