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Learned relational structure from dynamics fails to transfer to inverse problems, even when edge accuracy is high, due to uncaptured approximation errors.
Foundation models can decisively outperform classical methods in time series forecasting, but only under specific data conditions鈥攌nowing when to deploy them is crucial for efficiency.
