Researchers report turning the Platonic Representation Hypothesis (PRH) — the conjecture that models trained on different modalities converge to the same representation of the world as they scale — into a theorem, addressing what they describe as the hypothesis's lack of both a guarantee and a recipe without a proof. The work frames this convergence question as central to AI and presents the proof in connection with Joint Embedding Predictive Architectures (JEPAs).
Whether models trained on different modalities converge to the same representation of the world is central to AI. The Platonic Representation Hypothesis (PRH) conjectures that they do as they scale, but without a proof it offers neither a guarantee nor a recipe. We turn the hypothesis into a theorem
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