Junyou Zhu

Deep GNNs can achieve superior depth scaling and accuracy by leveraging controlled Turing instability or stable near-critical propagation to combat over-smoothing.

Explicitly modeling the temporal rate of change in spatio-temporal graph models unlocks superior prediction accuracy and pattern preservation for propagating phenomena.

Action verification can now be reliably performed in VLA models, reducing the risk of grasp failures and task errors in real-world robotic applications.