Mingxin Liu

Even state-of-the-art LMMs struggle with reasoning-informed visual editing, with the best model only achieving 56.6% accuracy on a new comprehensive benchmark.

Synergistic Information Disentanglement can significantly boost few-shot learning performance in computational pathology by preserving unique diagnostic signals across modalities.