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论文提出 GIST（Generator-state-Informed Supervision for Tabular Foundation Models），利用生成器中间状态的条件标签分布替代硬标签监督，以降低表格基础模型预训练中的采样方差，在 TabArena 和 TALENT 基准上取得更好的平均排名。
This paper explores adapting prior-data fitted networks (PFNs) for tabular anomaly detection, proposing a fine-tuning-free approach (ZEN) using frozen TabPFN features with nearest-neighbor scoring, and a fine-tuned method (FOCUS) that surpasses all baselines on the ADBench benchmark.
Proposes a continuity criterion for extending discrete-time causal prior-data fitted networks to continuous time using stochastic differential equations, introducing a taxonomy and fine-grid integration method that outperforms naive integration on irregular observation schedules.
This paper adapts classical class imbalance techniques to Prior-Data Fitted Networks (PFNs) for tabular classification, finding that thresholding and downsampling perform well due to PFNs' calibration and limited-data capabilities.
TabPFN-MT extends PFNs to multitask in-context learning for tabular data, achieving state-of-the-art on small-to-medium datasets while reducing inference cost from O(T) to O(1) forward passes.