We introduce a novel, scalable framework to evaluate compositional generalization, leverage it to evaluate more than 5k models, and propose a family of neural models pushing the Pareto frontier on this task.

The first benchmark for multi-factor sequential disentanglement representations, introduces a novel method, and leverages Vision-Language Models to automate annotation and evaluation—enabling scalable, label-free workflows.