This document introduces AXIOM, a novel artificial intelligence architecture designed to learn how to play games efficiently using object-centric models and active inference. Unlike traditional deep reinforcement learning (DRL)methods that require vast amounts of data, AXIOM leverages inherent priors about objects and their interactions, enabling it to master various games within 10,000 interaction steps. The system employs a Slot Mixture Model (sMM) for visual parsing, an Identity Mixture Model (iMM) for object classification, a Transition Mixture Model (tMM) for dynamics, and a Recurrent Mixture Model (rMM) to infer dynamic transitions, all of which adapt their complexity by growing and pruning components. This approach significantly reduces computational costs and offers interpretable world models compared to state-of-the-art DRL baselines, demonstrating robustness to environmental changes like color and shape perturbations.