Abstract: I will discuss recent work on a general class of high-dimensional factor regression models where each observation depends on interactions between a subset of the unknown parameters as well as covariate information. For any fixed number of interactions, we prove exact formulas for the high-dimensional limit of mutual information and the minimum mean-squared error. Our results provide a unified framework for analyzing a broad class of models, allowing for heteroskedastic noise and asymmetric interactions. This is joint work with Ricardo Rossetti.