Shao-Jie Zhang

The MaP is proposed, a unified framework that seamlessly harmonizes divergent GUI navigation tasks and effectively mitigates gradient conflicts and significantly outperforms the direct mixture training, establishing a robust paradigm for multi-task GUI navigation.

Real-valued pairwise supervision can dramatically improve clustering performance, as shown by ECI-PP's ability to outperform state-of-the-art methods in uncertain environments.