Pan Liu

Source-validation accuracy is a poor proxy for out-of-distribution reliability; re-ranking checkpoints by aggregated source reliability metrics significantly boosts target probability quality without needing target data.

Pretrained scientific foundation models can overcome massive chemical and instrumentation shifts entirely at test-time by using reference-guided retrieval to recalibrate latent predictions on the fly without query supervision.

UltraIR outperforms traditional machine-learning approaches in IR spectroscopy, achieving high accuracy with minimal labeled data across diverse chemical analysis tasks.