In autonomous driving, perceiving surrounding objects is not enough: failing to understand how they move and interact over time can lead to dangerous decisions. Evaluating vision-language models (VLMs) therefore requires testing whether they connect past spatial observations to future consequences. Yet existing benchma...
Lie-Qi Liu, Rui Gao, Jia-Chen Gu et al.· 0 citations
EmbodiedSkills is presented, a skill-based agent framework that augments frozen VLA policies with world action models for consequence-aware action selection and demonstrates the applicability of consequence-aware selection across different policy backends and action representations.
Wei Wang, Wen-Qiao Zhang, Yu-Tong Lin et al.· 2 citations
This work introduces WorldScope, which composes cross-view entity evidence and support-surface coverage into an image-subset evidence lattice, and proposes WorldFlow, which composes cross-view entity evidence and support-surface coverage into an image-subset evidence lattice.
Wei Wang, Wen-Qiao Zhang, Yu-Tong Lin et al.· 0 citations
EmbodiedSkills is a unified framework that treats each skill decision as an execution proposal: the runtime checks its prerequisites before execution and verifies the outcome afterward and provides a trainable and inspectable agent layer for turning low-level VLA policies into closed-loop embodied systems.
Wei Wang, Wen-Qiao Zhang, Yu-Tong Lin et al.· 1 citation
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