Spectral Audit of In-Context Operator Networks
Researchers have developed a new Jacobian-based spectral audit to evaluate neural operators and in-context operator learning models. This method goes beyond simple prediction error to assess the local dynamical structure, including sensitivities, frequency response, and stability. The audit can reveal failures in operator fidelity that might be missed by standard metrics, such as high-frequency degradation or prompt-operator inconsistencies, offering a more comprehensive diagnostic for learned operators. AI
IMPACT Provides a more robust evaluation framework for neural operators, potentially leading to more reliable and stable AI models in scientific domains.