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A residual-augmented probabilistic operator learning framework that casts flow-matching-based generative modeling in infinite-dimensional function spaces while leveraging inexpensive low-fidelity solution operators as an inductive bias and enables accurate characterization of uncertainty in low-data regimes.
EFE demonstrates that LLM-based evolution can improve both accuracy and interpretability when automatically tackling structured data, and is found to be particularly effective on classical decision trees.
The discovered that a complementary and previously unexploited in-span adaptation channel exists within the current reduced subspace, and obtains a trajectory-informed spectral preconditioner, in which the subspace is unchanged but the basis is reweighted and realigned toward the modes visited by the dynamics.
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