Yi Pan

Explicitly supervising physical state changes, not just language or latent representations, unlocks more robust VLA model generalization and real-world robotic control.

Sub-quadratic linear attention no longer requires full-precision state caching: 8-bit recurrent states can match FP32 accuracy while delivering up to 3.70× kernel speedups without any retraining.

Quantum machine learning enhances Q-matrix estimation by providing greater stability and robustness, even when classical methods achieve higher average accuracy.

ASTELD uncovers a critical gap in the autonomous AI landscape: no evaluated systems achieve both local-first deployment and enterprise-grade security.

Small digit changes can lead to large click misplacements, and MissClick exploits this to achieve unprecedented attack success rates against GUI grounding models.