Hierarchical Latent Structure Learning through Online Inference

This paper introduces HOLMES, a computational model that learns hierarchical (multi-level) structures from sequential data in real-time without needing labeled examples. The model combines statistical techniques to discover how experiences relate at different levels of abstraction, helping AI systems generalize knowledge while still distinguishing important details. In tests, HOLMES created more compact representations and transferred knowledge more effectively than simpler flat models.