2nd Workshop on Compositional Learning: Safety, Interpretability, and Agents

Compositionality, defined as the ability to construct and reason about complex concepts from reusable components, is a hallmark of human cognition and the key to robust generalization. Despite the astonishing progress of modern AI systems, it remains an open question whether they truly capture and leverage the compositional nature of many real-world domains. The workshop will explore this pressing challenge across multiple critical dimensions. We will invite contributions focusing on the theoretical foundations of compositionality, its central role in the age of foundation models and agents, and its impact on achieving robustness and systematic out-of-domain generalization. Through interdisciplinary dialogue, we aim to catalyze new research directions that push the boundaries of compositional learning in advanced AI systems.