KANEx

KANEx framework uses Kolmogorov-Arnold Networks to ground VLM explanations for medical imaging

Researchers introduce KANEx, a framework that leverages the symbolic transparency of Kolmogorov-Arnold Networks (KANs) to produce more faithful textual and visual explanations for chest X-ray classifiers. The system includes KAN-Map, a novel heatmap method derived directly from KAN model structure rather than gradient approximations, which feeds grounded context into downstream Vision-Language Models. Benchmarked on MIMIC-CXR, KAN-based architectures with ResNet/ViT backbones show a 10% improvement in visual localization and downstream reasoning quality over baseline approaches. The work positions mathematically interpretable architectural units as a path toward clinician-trustworthy medical AI.