Author

Jin-Yu Gu

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Preprint Sep 2026

Faster and Better? Benchmark Bugs and Design Limitations Distort the Evaluation of Vision-Language-Action Acceleration

Simulated manipulation benchmarks are the standard tool for evaluating vision-language-action (VLA) policies and the acceleration methods that reduce their inference latency for on-robot deployment. On these benchmarks, we observe that some training-free acceleration methods, which approximate the baseline policy's com...

Qi-Wei Chen, Kai-Jun Zhou, Nuo-Hui Shi et al. · 0 citations

#artificial intelligence Preprint Oct 2026

VLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models

Vision-Language-Action models (VLAs) have achieved high success rates on familiar manipulation tasks, yet it remains unclear whether such scores reflect genuine zero-shot generalization and perturbation robustness. Existing evaluation frameworks provide limited capability-level diagnosis and combinatorial perturbation...

Jun Xiao, Rui Ye, Dong-Run Cao et al. · 0 citations

#artificial intelligence Preprint Sep 2026

rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference

Factory work is a promising early scenario for embodied AI: assigning repetitive manual jobs to robots has clear economic payoff, and a structured station keeps the jobs tractable for current policies. Vision-Language-Action (VLA) models now dominate as the policy paradigm for these robots. The inference latency of VLA...

Kai-Jun Zhou, Zhi-Yang Li, Le Chen et al. · 0 citations