这篇论文用树结构搞定模型对少数子组表现差的问题，还能看清子组结构，挺实在的。
神经分类树(NCT)通过树状结构编码子组信息，无需子组标注即可将样本路由到“易”或“难”节点，并重用路径作为伪标签迭代优化。在五个基准（含二分类和多分类虚假关联）上，NCT一致隔离少数子组，解释性强，且鲁棒性与最先进方法相当。
Discovering Latent Groups for Robust Classification
Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented subgroups. Existing methods address this by adjusting network parameters, guided either by subgroup annotations or inferred pseudo-group labels. Yet at inference, these methods produce only a class prediction, with no insight into a sample's latent subgroup. We propose neural classification trees (NCT), a framework that achieves robustness by encoding subgroup structure in its tree-shaped architecture. By routing each sample to an "easy" or "hard" node of this tree -- based on prediction correctness -- and reusing these routes as pseudo-labels for the next iteration, NCT disentangles conflicting subgroups, without requiring subgroup supervision. We evaluate NCT on five benchmarks spanning binary and multi-class spurious correlations. Our experiments show that the learned tree topology provides strong interpretability by consistently isolating minority subgroups, which provides a transparent mapping between the model architecture and the data's latent group structure, while yielding competitive robustness with state-of-the-art methods.