Brief

Researchers have developed CascadeNet, a novel machine learning framework designed to recover hidden influence networks from cascade data without needing to specify a diffusion model. This approach uses a Jacobian-based method with Neyman-orthogonal debiasing to achieve accurate network inference. CascadeNet demonstrated superior performance in simulations across various data-generating processes and accurately mapped COVID-19 transmission networks in Spain, correlating well with mobility data, unlike existing methods. AI

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