Driving world-action models improve planning by coupling multimodal reasoning with future prediction, but their growing inference cost increasingly conflicts with the real-time latency requirements of vehicle control. Existing acceleration methods reduce tokens, layers, or sampling steps with policies selected prior to...
Xiao-Huan Pei, Heng-Guang Zhou, Yuan-Hao Ban et al.· 0 citations
Improvements are shown that ReCAST yields improvements that generalize beyond the training rewards and support its core principle: assigning each reward greater weight at the denoising timesteps where its feedback is most informative.
Yi-Hang Chen, Yuan-Hao Ban, Kuei-Chun Kao et al.· 0 citations
This work replaces the static critic with a feed-forward 4D reconstruction reward that explicitly models scene dynamics, allowing coherent motion to receive high consistency rewards, and adds a motion prior that rewards natural scene-flow magnitude while penalizing jitter and non-rigid artifacts.
Yuan-Hao Ban, Jia-Qi Feng, Heng-Guang Zhou et al.· 0 citations
Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models. Existing faithfulness benchmarks, however, rely on simple atomic instructions, on which top-tier systems already achieve near-perfect scores. As T2I models enter cre...
Yuanhao Ban, Tong Xie, Sohyun An et al.· arXiv.org· 0 citations
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