# Multi-Class Margin Regularization (MCM)

Evaluation of medical vision-language models has centered on accuracy, leaving whether their confidence is trustworthy largely untested. MVC-Bench splits calibration into three axes, robustness, effectiveness of calibration and prompt-tuning methods, and stability under prompt templates and random seeds, spanning fundus, histopathology and chest X-ray modalities, eight backbones and six prompt-tuning methods across 1,638 controlled experiments. In-domain calibration rankings barely survive domain shift, with a mean Kendall correlation of -0.0598 over 30 rank-transfer tests and none significantly positive; the proposed Multi-Class Margin regularizer reaches the lowest ECE in 10 of 12 in-domain settings but only 4 under domain shift.

Evaluation of medical vision-language models has centered on accuracy, leaving whether their confidence is trustworthy largely untested. MVC-Bench splits calibration into three axes, robustness, effectiveness of calibration and prompt-tuning methods, and stability under prompt templates and random seeds, spanning fundus, histopathology and chest X-ray modalities, eight backbones and six prompt-tuning methods across 1,638 controlled experiments. In-domain calibration rankings barely survive domain shift, with a mean Kendall correlation of -0.0598 over 30 rank-transfer tests and none significantly positive; the proposed Multi-Class Margin regularizer reaches the lowest ECE in 10 of 12 in-domain settings but only 4 under domain shift.