Yan Liang

Quantizing MoE models with mixed precision is significantly improved by calibrating expert error sensitivity using an information-theoretic contrast target, outperforming simple ranking-based methods.

Counterintuitively, the most *unreliable* samples in medical imaging datasets—those with fluctuating confidence and frequent forgetting during training—are the *most* informative for building accurate decision boundaries.