3 papers
cs.AI2026
REFLEX: Rethinking MoE Inference as Refinement-Aware Compute Allocation in Diffusion Language Models
Xiang Xia, Cheng Yan, Yiming Zhang +3
Mixture-of-experts (MoE) models increase parameter capacity by activating only a small subset of experts for each token. This conditional-computation paradigm has enabled autoregre…
cs.CV2025
Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos
Hao Luo, Yicheng Feng, Wanpeng Zhang +7
We introduce Being-H0, a dexterous Vision-Language-Action model (VLA) trained on large-scale human videos. Existing VLAs struggle with complex manipulation tasks requiring high dex…
cs.CV2025
Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning
Jiazheng Liu, Sipeng Zheng, Börje F. Karlsson +1
Multimodal large language models (MLLMs), built on large-scale pre-trained vision towers and language models, have shown great capabilities in multimodal understanding. However, mo…