GenZ: Hybrid statistical–foundational models for knowledge discovery from real-valued multidimensional targets

We present GenZ, a hybrid model that turns a foundational model (FM) into a {\bf knowledge-discovery engine} explaining variation in real-valued multidimensional targets. The FM is treated as a noisy oracle that can answer yes/no questions about a semantic item $s$ (text, image, or both) and GenZ learns {\bf which questions to ask} so that the answers $\bz$ explain the statistical link to a possibly high-dimensional target $\by$. Discovery is driven by {\bf group reasoning}: at each step, the model partitions items by the posterior of the current latent features, and asks the FM to articulate the semantic commonality that explains the divide. The resulting feature descriptors $\theta_f$ are the primary product---human-readable hypotheses about why $s$ predicts $\by$---while predictive accuracy is a secondary, but consistently strong, by-product (e.g.~beating both the 0-shot FM baseline and TabPFN on LLM embeddings of the same items in hedonic price regression). Across four domains---hedonic house pricing, Netflix cold-start recommendations, Arizona species ecology, and human visual-cortex fMRI---GenZ recovers dataset-specific structure that the FM does not surface from priors alone. Holding the items $s$ fixed and varying only the target $\by$ (Arizona species under spatial/taxonomic/functional targets; the same natural images under FFA/EBA/PPA brain responses) yields {\bf qualitatively different} discovered feature sets, demonstrating that GenZ characterizes the $s\!\to\!\by$ link rather than the marginal distribution of $s$.