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Graph transformers can be fundamentally limited by their tokenization strategy, as some tokenizations provably preclude efficient learning of structural representations realizable with other tokenizations.
Unlock the potential of Kolmogorov-Arnold Networks with WS-KAN, a weight-space architecture that understands their hidden symmetries and predicts their performance far better than generic methods.
