Model Detail
Olmo-Hybrid-7B
—LTBs-KAN: Linear-Time B-splines Kolmogorov-Arnold Networks
arXiv:2604.22034v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) are a recent neural network architecture offering an alternative to Multilayer Perceptrons (MLPs) with improved explainability and expressibility. However, KANs are significantly slower than MLPs due to the recursive n
Scaling of Gaussian Kolmogorov--Arnold Networks
arXiv:2604.21174v1 Announce Type: cross Abstract: The Gaussian scale parameter \(\epsilon\) is central to the behavior of Gaussian Kolmogorov--Arnold Networks (KANs), yet its role in deep edge-based architectures has not been studied systematically. In this paper, we investigate how \(\epsilon\) aff
Optimized Architectures for Kolmogorov-Arnold Networks
arXiv:2512.12448v2 Announce Type: replace Abstract: Efforts to improve Kolmogorov--Arnold networks (KANs) with architectural enhancements have been stymied by the complexity those enhancements bring, undermining the interpretability that makes KANs attractive in the first place. Here we study overpr
In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
arXiv:2603.15250v2 Announce Type: replace Abstract: Symbolic regression aims to replace black-box predictors with concise analytical expressions that can be inspected and validated in scientific machine learning. Kolmogorov-Arnold Networks (KANs) are well suited to this goal because each connection
Olmo Hybrid: From Theory to Practice and Back
arXiv:2604.03444v3 Announce Type: replace-cross Abstract: Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attention. Yet there is no consensus on whether the potential benefits
A Practitioner's Guide to Kolmogorov-Arnold Networks
arXiv:2510.25781v4 Announce Type: replace-cross Abstract: Kolmogorov-Arnold Networks (KANs), whose design is inspired-rather than dictated-by the Kolmogorov superposition theorem, have emerged as a structured alternative to MLPs. This review provides a systematic and comprehensive overview of the ra
