CoGe-GCD paper reframes generalized category discovery with compositional generalization

A newly announced arXiv paper presents CoGe-GCD, an approach to generalized category discovery, the task of sorting unlabeled data into both known and previously unseen classes. The work draws on compositional generalization, aiming to reuse primitives learned from labeled classes while detecting when novel combinations of those primitives point to new categories. It positions GCD as a challenge requiring human-like compositional reasoning in machine learning systems.