arXiv:2510.01663v2 Announce Type: replace-cross Abstract: For many real-world applications, understanding feature-outcome relationships is as crucial as achieving high predictive accuracy. While traditional neural networks excel at prediction, their black-box nature obscures underlying functional relationships. Kolmogorov--Arnold Networks (KANs) address this by employing learnable spline-based activation functions on edges, enabling recovery of symbolic representations while maintaining competitive performance. However, KAN's architecture presents unique challenges for network pruning. Convent
The paper addresses a current challenge in interpreting advanced neural networks like KANs, which are gaining traction for their interpretability while maintaining performance.
This research provides a method for understanding the contribution of individual features in interpretable AI models, crucial for trust, debugging, and regulatory compliance in complex applications.
The ability to perform reliable attribute scoring on Kolmogorov-Arnold Networks will enhance their practical applicability and adoption in fields requiring transparent decision-making.
- · AI developers
- · Auditors of AI systems
- · Industries requiring explainable AI
- · Kolmogorov-Arnold Network researchers
- · Black-box AI models in regulated sectors
Improved interpretability of KANs could lead to their wider adoption in critical applications.
Increased trust in AI systems could accelerate the deployment of autonomous decision-making in sensitive areas.
Enhanced explainability may foster new regulatory frameworks centered around transparent AI designs.
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Read at arXiv cs.AI