Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?
arXiv stat.ML1mo4 min read
arXiv:2606.31126v2 Announce Type: replace-cross Abstract: Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design. As strong pretrained encoders now supply rich fixed-length representations, the difficulty has shifted from representation learning to building a data-efficient predictor for the few-shot regime. Tabular foundation models such as TabPFN and TabICL are unlikely candidates for this role: they are in-context learners pretrained on synthetic tables drawn from random causal graphs, a generative prior with no
