arXiv:2609. 01956v2 Announce Type: replace Abstract: Kolmogorov-Arnold Networks (KANs) place learnable B-spline activations on network edges rather than fixed activations on nodes.
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FlashKAN introduces a new implementation of Kolmogorov‑Arnold Networks (KANs) that replaces the traditional Cox‑de Boor recursion with a truncated power form, allowing each uniform cubic B‑spline to be expressed as five shifted −(x)−^3 terms. The torch.compile‑fused implementation collapses these operations into a single GPU kernel, eliminating recursion, span lookup, and scatter‑gather steps. Additionally, the method includes a bounded‑coordinate stabilization to clamp inputs to [0, k+1], preventing catastrophic cancellation, and provides a production‑ready, open‑source package (pip install flashkan) as a drop‑in replacement for existing KAN layers.
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.
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.
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.
arXiv:2609.26067v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions, increasing flexibility but also parameter memory be...
arXiv:2608. 00737v1 Announce Type: new Abstract: Hard-constrained recurrent physics-informed networks (HRPINNs) embed known dynamics inside a recurrent numerical integrator and restrict a neural branch to learning only the residual dynamics that the first-principles model does not capture.