Title: MetaCluster: Enabling Deep Compression of Kolmogorov-Arnold Network
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: KAN, Weight Sharing, Compression

Abstract: Kolmogorov-Arnold Networks (KANs) replace scalar weights with per-edge vectors of basis coefficients, thereby boosting expressivity and accuracy but at the same time resulting in a multiplicative increase in parameters and memory. We propose MetaCluster, a framework that makes KANs highly compressible without sacrificing accuracy. Specifically, a lightweight meta‑learner, trained jointly with the KAN, is used to map low‑dimensional embedding to coefficient vectors, shaping them to lie on a low‑dimensional manifold that is amenable to clustering. We then run K-means in coefficient space and replace per‑edge vectors with shared centroids.  Afterwards, the meta‑learner can be discarded, and a brief fine‑tuning of the centroid codebook recovers any residual accuracy loss. The resulting model stores only a small codebook and per-edge indices, exploiting the vector nature of KAN parameters to amortize storage across multiple coefficients. On MNIST, CIFAR-10, and CIFAR-100, across standard KANs and ConvKANs using multiple basis functions, MetaCluster achieves a reduction of up to $80\times$ in parameter storage, with no loss in accuracy. Code will be released upon publication.