2 papers
cs.DC2026
vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models
Peiqi Yin, Jiangyun Zhu, Han Gao +13
Any-to-any multimodal models that jointly handle text, images, video, and audio represent a significant advance in multimodal AI. However, their complex architectures (typically co…
cs.RO2025
Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning
Sheng Chen, Peiyu He, Jiaxin Hu +67
Modern robot navigation systems encounter difficulties in diverse and complex indoor environments. Traditional approaches rely on multiple modules with small models or rule-based s…