arXiv Machine Learning September 23, 2026 By Kazi Ahmed Asif Fuad, Lizhong Chen

FuncCode: Compressing Kolmogorov--Arnold Networks in Function Space with Hardware-Aware Quantization

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arXiv Machine Learning

Aug 4

SparseKAN: Compressing Kolmogorov--Arnold Networks Across Basis Functions, Neurons, and Bits

arXiv:2608. 00859v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions parameterized by multiple basis coefficients.

By Kazi Ahmed Asif Fuad, Lizhong Chen

efficiency

arXiv AI

Jul 20

Kolmogorov--Arnold Networks for Small Language Models

arXiv:2607. 15525v1 Announce Type: cross Abstract: Kolmogorov--Arnold Networks (KANs) replace fixed node activations with learned one-dimensional edge functions, offering an explicit interface for interpretation and a possible alternative to transformer feed-forward networks.

By Felippe Alves, Renato Vicente

llms efficiency benchmarks

arXiv Machine Learning

Aug 4

BiKAN: Restoring Collapsed Basis of Binary Kolmogorov--Arnold Networks

arXiv:2608. 01490v1 Announce Type: new Abstract: Binarizing a polynomial Kolmogorov--Arnold Network (KAN) not only changes parameter precision, but also alters the function space available to each layer.

By Kazi Ahmed Asif Fuad, Lizhong Chen

arXiv Machine Learning

Jun 16

QuantKAN: A Unified Quantization Framework for Kolmogorov Arnold Networks

arXiv:2511. 18689v3 Announce Type: replace Abstract: Kolmogorov--Arnold Networks (KANs) replace linear weights with spline-based functions, offering strong expressivity but posing challenges for low-precision deployment due to heterogeneous parameter distributions.

By Kazi Ahmed Asif Fuad, Lizhong Chen

efficiency benchmarks

arXiv Machine Learning

Aug 27

Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality

arXiv:2608. 25807v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) replace fixed activations in deep architectures with learnable univariate edge functions, making the choice of edge parametrisation central.

By K S Sesh Kumar

benchmarks

Hugging Face Trending Papers

Aug 26

Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality

Kolmogorov-Arnold Networks (KANs) replace fixed activations in deep architectures with learnable univariate edge functions, making the choice of edge parametrisation central. Existing variants rely on...

benchmarks

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Related stories

SparseKAN: Compressing Kolmogorov--Arnold Networks Across Basis Functions, Neurons, and Bits

Kolmogorov--Arnold Networks for Small Language Models

BiKAN: Restoring Collapsed Basis of Binary Kolmogorov--Arnold Networks

QuantKAN: A Unified Quantization Framework for Kolmogorov Arnold Networks

Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality

Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality