Qi Zhu

By explicitly disentangling shared and view-specific features across multi-view fundus images, MVGFDR achieves superior diabetic retinopathy grading compared to methods that directly fuse visual features.

Forget fine-tuning: inject targeted time-series insights into general LLMs and watch their reasoning skills skyrocket by up to 26%.

LLMs can be steered more effectively by viewing activation manipulation through the lens of ordinary differential equations and control theory, leading to significant gains in alignment benchmarks.