Beyond Linear Superposition: Discovering Climate Features in AI Weather Models with KAN-SAE

In three linesKAN-SAE, a sparse autoencoder using nonlinear B-spline activations from Kolmogorov-Arnold Networks, discovers 975 climate features in weather prediction models (vs 566 for linear SAEs). Without climate supervision, it identifies interpretable phenomena like European heatwaves and western Pacific typhoons confirmed by causal steering.

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