KANEx Swaps Post-Hoc VLM Narration for Kolmogorov-Arnold Network Splines in Chest X-Ray Explanation

arXiv 2607.24730·low signalXBlueskyLinkedInCopy link

KANEx (arXiv 2607.24730, July 27) targets a specific failure in clinical AI deployment: chest X-ray classifiers are increasingly paired with Vision-Language Models that generate fluent natural-language explanations, which adds linguistic polish without touching the opacity of the underlying visual model. The work instead builds on Kolmogorov-Arnold Networks, whose spline-based components are intrinsically inspectable, and translates that mathematical interpretability into clinician-facing medical explainability. The broader point generalizes past radiology: an LLM narrating a black box produces plausible text, not grounded explanation — a distinction worth holding onto whenever an agent is asked to explain another model's output.