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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.AI2026
Some Large Language Models Exhibit Consistent Risk Attitudes
Bowen Sun, Rui Min, Yuxi Wang +3
As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action. We test…
cs.RO2026
PRISM: PRior-guided Imagination Sampling in world Models
Yuhai Wang, Jiawei Xia, Rongxuan Zhou +5
Latent world models learn representations that enable them to predict how actions change the environment - a capability we call physical intuition. We find that these same represen…