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This paper introduces STKAN, a spatio-temporal forecasting architecture that integrates Taylor-polynomial Kolmogorov-Arnold Network modules for spatial and temporal token mixing. Experiments on five traffic benchmarks show competitive performance, suggesting nonlinear function approximators can complement architectural design.