A foundation model of numerical intelligence with cross-disciplinary generalization
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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.
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A foundation model of numerical intelligence with cross-disciplinary generalization
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Semantic Scholar citations 0 · Semantic Scholar influential citations 0
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.