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Multi-modal alignment in symbolic regression models like SNIP doesn't actually improve during optimization, suggesting current approaches are too coarse to effectively guide symbolic search.
Tabular foundation models can now tackle multivariate time series forecasting in a zero-shot manner, outperforming existing tabular methods by cleverly reframing the problem as a series of scalar regressions.
