Zero- and Few-Shot Arbitrary Conditioning with Semantic and Predictive Context

Generalist statistical inference requires answering conditional queries whose targets and observed features may change across previously unseen schemas. We study a zero- and few-shot arbitrary conditioning problem over semantically described tabular datasets, where observed feature–value pairs alone may leave the target distribution ambiguous. We introduce the Arbitrary Set-based Permutation-Invariant Reasoning Engine (ASPIRE), which draws on feature and dataset semantics, optional support examples, and external predictive context. ASPIRE represents observed values and external forecast probabilities as semantically grounded atoms, contextualizes co-occurring atoms with a shared permutation-equivariant module, and combines distinct sources of evidence through role-aware late fusion. Target-native likelihood heads return categorical probability mass functions or continuous densities; the same conditional interface supports chain-rule table generation. Trained on over 1,400 real-world datasets, ASPIRE leads the evaluated zero-shot comparisons. With five support examples, it achieves 12.7% higher F1 and 5.9% lower normalized RMSE than the strongest competing methods. External predictive context yields further gains, while the standalone conditional model also achieves the strongest five-shot table-generation fidelity among the evaluated methods.