This paper introduces KAMoE, a novel Mixture of Experts (MoE) framework based on Gated Residual Kolmogorov-Arnold Networks (GRKAN). We propose GRKAN as an alternative to the traditional gating function, aiming to enhance efficiency and interpretability in MoE modeling. Through extensive experiments on digital asset markets and real estate valuation, we demonstrate that KAMoE consistently outperforms traditional MoE architectures across various tasks and model types. Our results show that GRKAN exhibits superior performance compared to standard Gating Residual Networks, particularly in LSTM-based models for sequential tasks. We also provide insights into the trade-offs between model complexity and performance gains in MoE and KAMoE architectures.

翻译：本文提出了一种基于门控残差Kolmogorov-Arnold网络（GRKAN）的新型专家混合（MoE）框架KAMoE。我们将GRKAN作为传统门控函数的替代方案，旨在提升MoE建模的效率和可解释性。通过在数字资产市场和房地产估值领域的广泛实验，我们证明KAMoE在各种任务和模型类型中均持续优于传统MoE架构。实验结果表明，与标准门控残差网络相比，GRKAN展现出更优越的性能，尤其是在处理序列任务的LSTM模型中。我们还深入探讨了MoE与KAMoE架构中模型复杂度与性能增益之间的权衡关系。