Author

Jingkai Jia

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Preprint Sep 2026

Beyond Token Importance: Preserving Spatial Scaffolds for Efficient Vision-Language-Action Inference

Existing VLA pruning strategies primarily select individual visual tokens according to task-level semantic relevance, while overlooking the spatial information required for robotic manipulation. To examine this limitation, we construct a simple Stride baseline that uniformly samples tokens along the flattened one-dimen...

Jia-Yu Chen, Shuyong Gao, Jing-Kai Jia et al. · 0 citations

Preprint Jul 2026

EDAR: Learning Environment-Dependent Action Representations for Robotic Manipulation

Experiments on simulated and real-robot manipulation benchmarks demonstrate that EDAR improves downstream policy learning, especially in long-horizon manipulation, highlighting the importance of grounding action representations in executable control structure and environment-conditioned visual change.

Yuecheng Xu, Tong Yang, Jingkai Jia et al. · 0 citations