Recent foundational models for tabular data, such as TabPFN, excel at adapting to new tasks via in-context learning, but remain constrained to a fixed, pre-defined number of target dimensions-often necessitating costly ensembling strategies. We trace this constraint to a deeper architectural shortcoming: these models lack target equivariance, so that permuting target dimension orderings alters their predictions. This deficiency gives rise to an irreducible "equivariance gap", an error term that introduces instability in predictions. We eliminate this gap by designing a fully target-equivariant architecture-ensuring permutation invariance via equivariant encoders, decoders, and a bi-attention mechanism. Empirical evaluation on standard classification benchmarks shows that, on datasets with more classes than those seen during pre-training, our model matches or surpasses existing methods while incurring lower computational overhead.

翻译：近期涌现的表格数据基础模型（如TabPFN）在通过上下文学习适应新任务方面表现出色，但其目标维度数量仍受限于预定义的固定值——这通常需要采用计算成本高昂的集成策略。我们通过深入分析发现，这一限制源于更深层的架构缺陷：此类模型缺乏目标等变性，导致目标维度顺序的置换会改变其预测结果。该缺陷会产生不可约的“等变性间隙”，这种误差项会引发预测的不稳定性。我们通过设计完全目标等变的架构来消除此间隙——借助等变编码器、解码器及双向注意力机制确保置换不变性。在标准分类基准上的实证评估表明，对于类别数量超过预训练所见范围的数据集，我们的模型在保持较低计算开销的同时，达到或超越了现有方法的性能。