MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination
MARGIN is a runtime calibration method that learns model-specific confidence corrections from observed answer outcomes without retraining models or needing a separate calibration set. It tracks accuracy and stated confidence within defined bands, using their ratio to adjust reported confidence dynamically. This allows the system to adapt its reliability estimates during workload shifts.