The Rank-One Corner: How Much Value Equivalence Does a Task Need from a World Model?

arXiv:2607.06640v1 Announce Type: new Abstract: A learned world model is usually judged by how faithfully it reconstructs its observations or predicts reward, as though quality were something the model simply has or lacks. But what a task actually needs from a model is narrower: the few predictive coordinates its queries depend on, which we call the closure. We show that how much of that closure a latent comes to represent is set not by the model's capacity or its observations but by the dimensi...