Cortico-Cerebellar Coordination Flexibly Recombines Reusable Representations for Zero-Shot Compositional Generalization

Compositionality, the ability to flexibly recombine learned components to solve novel tasks, is a hallmark of biological intelligence and a central challenge for artificial systems. However, the circuit mechanisms and dynamical principles underlying compositional generalization remain poorly understood. Inspired by distributed cortico-cerebellar architecture, we develop a computational framework that couples recurrent cortical dynamics with adaptive cerebellar feedback. We evaluate this framework across cognitive and motor tasks requiring zero-shot execution of novel rule combinations and motor primitive sequences. While cortex-only models achieve accurate performance on learned compositions but fail to recombine previously acquired components in unseen contexts, cortico-cerebellar models exhibit robust zero-shot compositional generalization across both domains. Mechanistic analyses reveal that cortex-only models retain task-relevant latent representations but fail to flexibly deploy them under novel cognitive contexts and exhibit predecessor-dependent state carry-over during motor transitions. In contrast, cerebellar feedback stabilizes reusable cortical representations, steers successor dynamics, and adaptively regulates state transitions during novel compositional execution. Together, these mechanisms enable cortico-cerebellar models to achieve robust zero-shot compositional generalization across temporal and structural task variations. Our findings reveal a circuit-level mechanism for flexible compositional computation and establish a bio-inspired principle for developing artificial systems capable of zero-shot generalization.