Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data

• URL: http://arxiv.org/abs/2608.12989v1
• Date: Thu, 13 Aug 2026 09:12:02 GMT
• Title: Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data
• Abstract summary: This paper introduces Balanced Adaptive Prototype Selection (BAPS), a framework for constructing compact, information-preserving contexts for scalable TabPFN inference.<n> Experiments on the million-row HIGGS and SUSY datasets show that 512 prototypes retain strong predictive performance and reliable calibration.
• Score: 1.9768907237963547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context. This paper introduces Balanced Adaptive Prototype Selection (BAPS), a framework for constructing compact, information-preserving contexts for scalable TabPFN inference. Without modifying or retraining the pretrained model, BAPS jointly preserves representative structure, informative decision boundaries, local density, class balance, and feature-space diversity. Experiments on the million-row HIGGS and SUSY datasets show that 512 prototypes retain strong predictive performance and reliable calibration, corresponding to an approximately 1,953-fold context compression. All experiments were conducted on an Intel Core i7 CPU with 16 GB RAM and no GPU acceleration. These findings establish effective context construction as a practical mechanism for extending pretrained tabular foundation models to million-scale datasets.

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