A foundation model of numerical intelligence with cross-disciplinary generalization

TL;DR - UNICON is a foundation model that learns predictive relationships from graph-based numerical examples and generalizes across scientific and social systems. It approaches specialist performance without retraining, including in disciplines absent from training.

• Infers shared predictive operators from in-context examples and applies them to new queries.
• Uses one model across multiple disciplines rather than training task-specific specialists.
• Pairing UNICON with language-model agents reportedly surpasses state-of-the-art specialists in an unseen discipline.
• Greater training-corpus diversity improves cross-disciplinary generalization.