Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional multilayer perceptrons by replacing linear weights with learnable univariate functions. Despite their theoretical advantages in interpretability and expressiveness, practical research of KANs remains difficult due to high computational costs and inconsistent feature support across existing frameworks. This paper introduces KANLib, a modular, extensible, and computationally efficient framework for developing and evaluating KAN architectures. KANLib unifies core concepts from existing implementations, including PyKAN, EfficientKAN, and FastKAN, within a consistent software architecture that emphasizes flexibility, feature parity, and high performance. The framework supports two basis function types, adaptive grid rescaling, grid extension, and fine-grained architectural customization while maintaining compatibility with standard PyTorch workflows. Experimental evaluation on the California Housing benchmark demonstrates that KANLib reproduces the predictive behavior of established reference KAN implementations while achieving competitive computational efficiency. Furthermore, the framework enables the exploration of architectural variations beyond standard KAN formulations with only minor impacts on predictive performance. Overall, KANLib provides a robust foundation for future research on scalable and extensible KAN architectures.

翻译：科尔莫戈罗夫-阿诺德网络（KANs）近期作为传统多层感知器的一种有前景的替代方案出现，其通过使用可学习单变量函数替代线性权重。尽管KAN在可解释性和表达能力方面具有理论优势，但由于高昂的计算成本以及现有框架间特征支持不一致，其实际研究仍面临困难。本文介绍了KANLib，一个用于开发和评估KAN架构的模块化、可扩展且计算高效的框架。KANLib在强调灵活性、特征一致性和高性能的统一软件架构中，整合了现有实现（包括PyKAN、EfficientKAN和FastKAN）的核心概念。该框架支持两种基函数类型、自适应网格缩放、网格扩展以及细粒度架构定制，同时保持与标准PyTorch工作流的兼容性。在加州住房基准数据集上的实验评估表明，KANLib在复现已有参照KAN实现预测行为的同时，实现了具有竞争力的计算效率。此外，该框架支持探索超出标准KAN公式的架构变体，且对预测性能影响甚微。总体而言，KANLib为未来关于可扩展和可扩展KAN架构的研究提供了稳健基础。