Consero: Knowledge-Informed Prediction Framework with Language and Tabular Foundation Models

Real-world tabular prediction often requires both numerical observations and domain knowledge, yet translating language- generated relations into useful predictive information remains challenging. We present Consero, a knowledge-informed prediction framework integrating language and tabular foundation models through a common workflow of knowledge proposal, predictive execution, and feedback-driven revision. At its core, a knowledge–data encoder combines language-generated relation matrices with support-set evidence about feature interactions and prediction residuals within a predictive model that constructs corrections to a frozen tabular backbone. We pretrain this model on synthetic tasks spanning diverse mechanisms and knowledge reliability levels, with controlled experiments demonstrating its ability to exploit interaction evidence. Evaluation on 156 real-world classification and regression tasks shows that the complete framework, including task-specific adaptation and selection, achieves higher mean classification macro-F1 and lower mean normalized root mean squared error in regression than the unadapted backbone.