KANs vs MLPs: Statistical Gains in Accuracy Come with Higher Computational Cost

Jul 16, 2026

A new preprint benchmarks Kolmogorov-Arnold Networks (KANs) against Multi-Layer Perceptrons (MLPs) on 12 structured tabular classification tasks. The study finds that KANs achieve statistically significant accuracy improvements over MLPs, particularly in binary and multiclass settings, but require substantially more parameters and computational resources. The authors recommend KANs for high-precision needs and MLPs for efficiency in resource-limited scenarios.

Why it matters: This work provides empirical evidence to inform model selection for structured data, clarifying the trade-off between accuracy and computational efficiency when choosing between KANs and MLPs.

Full story at: arXiv Machine Learning ↗