The paper reports an empirical scalability study of data‑parallel training for Kolmogorov‑Arnold Networks (KANs) on high‑performance computing systems. Using up to eight NVIDIA A100 GPUs across four nodes on the FinisTerrae III supercomputer, the authors evaluate strong and weak scaling, communication overhead, and model‑size scaling, finding a 74.7% parallel efficiency and a 5.97× speedup at eight GPUs. They observe non‑monotonic communication costs driven by All‑Reduce choices and inter‑node latency, and note that while the parameter‑to‑memory ratio improves with larger models, training time scales less favorably, leading to guidelines for GPU topology and model‑size selection.
By Guangneng Chen, David Garcia Selfa, Pablo Quesada Barriuso
arXiv:2512. 12850v3 Announce Type: replace-cross Abstract: Low-latency, resource-efficient neural network inference on FPGAs is essential for applications demanding real-time capability and low power.
By Duc Hoang, Aarush Gupta, Philip Harris
arXiv:2512. 09084v3 Announce Type: replace Abstract: The Kolmogorov-Arnold representation theorem offers a theoretical alternative to Multi-Layer Perceptrons (MLPs) by placing learnable univariate functions on edges rather than nodes.
By Oscar Eliasson
arXiv:2604. 03345v2 Announce Type: replace Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful architecture for various machine learning applications.
By Bilal Khalid, Pedro Freire, Sergei K. Turitsyn, Jaroslaw E. Prilepsky
arXiv:2602. 02056v3 Announce Type: replace-cross Abstract: Ultrafast online learning is essential for high-frequency systems, such as controls for quantum computing and nuclear fusion, where adaptation must occur on sub-microsecond timescales.
By Duc Hoang, Aarush Gupta, Philip Harris
arXiv:2606. 17927v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear weights with learnable univariate functions.
By Julian Hoever, Gregor Schiele
arXiv:2606. 17927v2 Announce Type: replace-cross Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear weights with learnable univariate functions.
By Julian Hoever, Gregor Schiele
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
arXiv:2607. 18290v1 Announce Type: cross Abstract: In recent years, Kolmogorov-Arnold Networks (KANs) have attracted increasing attention due to their effectiveness in machine learning and scientific computing tasks, offering a new paradigm for neural network design.
By Hoang-Thang Ta
The paper introduces Temporal Kolmogorov‑Arnold Networks (T‑KAN) for forecasting high‑frequency limit order book data, replacing fixed linear weights in LSTMs with learnable B‑spline activation functions. This approach captures the shape of market signals, yielding a 19.1% relative improvement in F1‑score at a 100‑step horizon and a 132.48% return versus a -82.76% drawdown for DeepLOB under 1.0 bps transaction costs. T‑KAN also offers interpretability through visible dead‑zones in the splines and is optimized for low‑latency FPGA deployment via High‑Level Synthesis.
By Ahmad Makinde
arXiv:2604. 21174v3 Announce Type: replace-cross Abstract: Kolmogorov-Arnold Networks (KANs) replace fixed activations with learnable univariate edge functions whose behavior depends strongly on the chosen basis.
By Amir Noorizadegan, Sifan Wang, Leevan Ling
arXiv:2607. 13413v1 Announce Type: cross Abstract: This study presents an empirical benchmarking comparison between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) on structured tabular classification tasks.
By Matthew Steven P. Toledo, Justine Raphael H. Jacinto, Vivekjeet Singh Chambal, Rodolfo C. Camaclang III, Jamlech Iram N. Gojo Cruz, Reginald Neil C. Recario