3 papers
cs.RO2026
RoboFL: Federated Expert Assembly for World Action Models
Rongyu Zhang, Ruizhi Fan, Yunfan Lou +8
Vision-language-action and world-action models are increasingly popular, yet remain bottlenecked by physical interaction data that is scarce, institutionally siloed, and task-heter…
cs.CV2025
SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models
Hengyu Fang, Yijiang Liu, Yuan Du +2
Vision-Language-Action (VLA) models exhibit unprecedented capabilities for embodied intelligence. However, their extensive computational and memory costs hinder their practical dep…
cs.LG2025
FBQuant: FeedBack Quantization for Large Language Models
Yijiang Liu, Hengyu Fang, Liulu He +4
Deploying Large Language Models (LLMs) on edge devices is increasingly important, as it eliminates reliance on network connections, reduces expensive API calls, and enhances user p…