Kolmogorov Arnold Networks and Multi-Layer Perceptrons: A Paradigm Shift in Neural Modelling

2026-01-15

AI Summary

This study addresses the longstanding trade-off between accuracy and efficiency in conventional neural networks by systematically comparing Kolmogorov–Arnold Networks (KANs) with Multilayer Perceptrons (MLPs). Built upon the Kolmogorov representation theorem, KANs employ learnable spline-based activation functions within a grid-structured architecture, achieving both high accuracy and low computational cost. Experimental results across diverse tasks—including nonlinear function approximation, time series forecasting, and multivariate classification—demonstrate that KANs consistently outperform MLPs in predictive performance while significantly reducing floating-point operations (FLOPs). These findings position KANs as an interpretable, efficient, and accurate alternative architecture, particularly well-suited for resource-constrained and real-time applications.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The research undertakes a comprehensive comparative analysis of Kolmogorov-Arnold Networks (KAN) and Multi-Layer Perceptrons (MLP), highlighting their effectiveness in solving essential computational challenges like nonlinear function approximation, time-series prediction, and multivariate classification. Rooted in Kolmogorov's representation theorem, KANs utilize adaptive spline-based activation functions and grid-based structures, providing a transformative approach compared to traditional neural network frameworks. Utilizing a variety of datasets spanning mathematical function estimation (quadratic and cubic) to practical uses like predicting daily temperatures and categorizing wines, the proposed research thoroughly assesses model performance via accuracy measures like Mean Squared Error (MSE) and computational expense assessed through Floating Point Operations (FLOPs). The results indicate that KANs reliably exceed MLPs in every benchmark, attaining higher predictive accuracy with significantly reduced computational costs. Such an outcome highlights their ability to maintain a balance between computational efficiency and accuracy, rendering them especially beneficial in resource-limited and real-time operational environments. By elucidating the architectural and functional distinctions between KANs and MLPs, the paper provides a systematic framework for selecting the most suitable neural architectures for specific tasks. Furthermore, the proposed study highlights the transformative capabilities of KANs in progressing intelligent systems, influencing their use in situations that require both interpretability and computational efficiency.

Problem

• Kolmogorov-Arnold Networks
• Multi-Layer Perceptrons
• nonlinear function approximation
• computational efficiency
• time-series prediction

Innovation

• Kolmogorov-Arnold Networks
• spline-based activation
• computational efficiency
• function approximation
• neural architecture