What Is Worth Representing? Representational Empowerment for Continual Model Construction

The first problem of modeling the world is not just estimating the right parameters or causal structure, but deciding *what should be represented at all*. We frame this as *continual model construction*: an agent maintains an environment-specific model $M$ of an inaccessible world $W$ and curates a persistent library $\mathcal{L}$ of reusable representational elements across environments. We propose *Representational Empowerment* ($\operatorname{RepEmp}$) to score candidate elements by how much they expand the agent's future capacity to model and plan---a counterpart to classical environmental empowerment, redirected from control over external states to control over internal representations. We realize the framework as a hierarchical Actor-Curator architecture and test it across three experiments. In a finite-vocabulary causal-learning task, human participants construct causal models at varying abstraction levels to maximize goal reachability rather than fidelity to the world---a signature better predicted by $\operatorname{RepEmp}$ over information-gain or novelty alternatives. Matched simulations reveal $\operatorname{RepEmp}$-guided construction, not exploration, to be the driver of sufficient structure recovery and cross-task transfer. Finally, in an open-vocabulary planning domain, an LLM-augmented Curator builds more compact symbolic libraries, which also generalize better than baselines. Ablating $\operatorname{RepEmp}$ eliminates these benefits. Together, these results identify $\operatorname{RepEmp}$ as a potential principle for continual model construction: deciding what to build, retain, and reuse under bounded resources.