Fine-grained visual perception in MLLMs is commonly improved by raising the resolution, but the added visual tokens inflate vision-encoding and language-model prefilling costs. We show that the two operations underlying fine-grained perception, localizing the region of interest (RoI) and recognizing its content, have d...
Yuheng Shi, Xiao-Huan Pei, Minjing Dong et al.· 0 citations
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
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
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