3 papers
cs.RO2026
VLAQuantBench: Closed-Loop Evaluation of Post-Training Quantization for Vision-Language-Action Models
Jiuyi Xu, Qing Jin, Meida Chen +3
Post-training quantization reduces the memory requirements of vision-language-action (VLA) models, but precision selection must account for the interaction between layer scope, num…
cs.RO2026
Predict Before You Deploy: Offline Prediction of Quantization-Induced Task Degradation for World Action Models
Jiuyi Xu, Jinjia Guo, Meida Chen +2
World action models (WAMs) rely on video-generation backbones, requiring substantial memory and compute for deployment. Post-training quantization reduces memory and can accelerate…
cs.CV2025
LowDiff: Efficient Diffusion Sampling with Low-Resolution Condition
Jiuyi Xu, Qing Jin, Meida Chen +3
Diffusion models have achieved remarkable success in image generation but their practical application is often hindered by the slow sampling speed. Prior efforts of improving effic…