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Calibration in medical vision-language models is crucial, and MVC-Bench reveals that a simple train-time calibration method can outperform existing approaches in most scenarios.
Existing continual learning methods falter in preserving knowledge across diverse medical tasks, revealing critical gaps in their application to real-world clinical settings.
PolyAlign reveals that aligning models to context-specific human response distributions significantly enhances naturalness and faithfulness in multilingual interactions.
