Forms as Endpoints, Not Origins: An Explanatory Reversal of the Platonic Representation Hypothesis

The Platonic Representation Hypothesis (PRH) reports that independently trained neural networks converge on geometrically similar internal representations---a finding its authors interpret as evidence that learning systems approximate pre-existing, mind-independent abstract structures. This paper argues against that interpretation on two connected levels. First, the Platonic ontology is explanatorily unnecessary: convergence is adequately accounted for by the theory of convergent attractors under shared physical constraints, without positing any independent abstract realm. Second, the paper explains why the Platonic interpretation nevertheless feels compelling: the maximal robustness of certain attractors---their stability across all sufficiently expressive optimization processes---generates a phenomenology of necessity and mind-independence that naturally, but mistakenly, invites an inference to ontological independence. Drawing on structural realism, dynamical systems theory, and naturalist philosophy of mathematics, this paper proposes the Reversed Platonic Representation Hypothesis (RPRH): convergent representations are emergent fixed points produced by optimization under shared constraints, not ontologically prior structures toward which learning approximates; and the intuition that they must exist independently is itself a structurally predictable cognitive consequence of their robustness---inevitable, given the loop structure and the robustness condition, but not a matter of strict logical entailment. Convergent structures are real, but their reality is the reality of attractor patterns, not of a separate ontological realm.