What Is Worth Representing? Representational Empowerment for Continual Model Construction

Researchers have proposed a framework for continual model construction that shifts the focus of AI development from optimizing existing parameters to determining which environmental features are worth representing. By automating the selection of what an agent should prioritize, this approach aims to improve how autonomous systems adapt to new information over time. This work addresses a fundamental challenge in artificial intelligence: deciding which aspects of a complex world are relevant enough to be included in a model's internal representation.