Yufei Chen

LEAP reveals that separating evidence evaluation can drastically enhance the accuracy and interpretability of probabilistic forecasts in LLM applications.

Achieving precise medical image segmentation with limited labeled data is now possible by embracing intra-class heterogeneity through innovative prototype learning.

Prefix Retention Optimization boosts target identifier retention in multimodal generative retrieval, closing the critical indexing-decoding gap.

Overcome the limitations of existing semi-supervised segmentation methods by learning structural consensus across samples, achieving more generalizable pancreas segmentation under sparse supervision.