# SineKAN might actually beat B-splines for certain KAN tasks > Source: > Published: 2026-08-18 18:49:04+00:00 # SineKAN might actually beat B-splines for certain KAN tasks ## Why sinusoidal activations matter The core idea behind KANs is replacing the fixed activation functions on nodes with learnable functions on the edges. In the standard implementation, these are usually B-splines. However, B-splines can be computationally heavy and sometimes struggle with extrapolation outside their defined grid. SineKAN replaces these with sine functions, which effectively turns the network into a series of learnable frequency modulations. From a technical standpoint, this is an interesting pivot. Sinusoids are naturally suited for capturing periodic patterns, but they also have a unique way of representing high-frequency components that splines might need many more parameters to capture. If you are building an AI workflow for physics-informed neural networks (PINNs) or signal processing, this approach is significantly more intuitive than forcing a spline to fit a wave. ## Practical implementation and deployment If you want to get a hands-on guide on how this differs from a standard MLP or KAN, the main shift is in the weight update. Instead of updating a spline coefficient, the network optimizes the frequency and phase of the sine wave. This can lead to much faster convergence on specific types of mathematical functions. For those looking for a deep dive into the architecture, the implementation usually follows this logic: 1. The input is multiplied by a learnable weight (frequency). 2. This value is passed through a $\sin(x)$ function. 3. A residual connection or a linear term is often added to maintain stability and prevent the network from getting stuck in local minima caused by the periodicity. ## Comparing SineKAN to Standard KANs **Computational Overhead:** SineKAN is generally leaner because calculating a sine function is computationally cheaper than evaluating a B-spline basis.**Parameter Efficiency:** It often requires fewer parameters to represent oscillatory functions compared to the grid-based approach of original KANs.**Convergence Speed:** It can converge faster on periodic datasets but may struggle with monotonic functions that a spline would handle effortlessly.**Extrapolation:** Sinusoids extrapolate periodically, which is either a huge advantage or a huge liability depending on your specific real-world data. For anyone trying to implement this from scratch, the GitHub repository provides a solid starting point for deployment. The math is handled in the activation layer, meaning you can swap it into existing KAN frameworks relatively easily. It's a great example of how small changes in the activation function can fundamentally alter the behavior of an LLM agent or a regression model. ``` https://arxiv.org/abs/2407.04149 ``` [Stop letting your GPU idle while your CPU struggles to feed it 22h ago](/en/news/6724/) [Stop expecting LLMs to be databases because they are 2d ago](/en/news/6543/) [Since the provided content was only a title 3d ago](/en/news/6403/) [DeepMind WeatherNext actually predicts cyclones with scary 9d ago](/en/news/5584/) [Meta's AI actually broke into another company's system during a 10d ago](/en/news/5475/) [Meta AI accidentally hacked another company and it's a wild look 11d ago](/en/news/5398/) [Next Fitting a diffusion model into 264KB of RAM is actually possible →](/en/news/6833/)