The Strong Platonic Representation Hypothesis
Translation is just geometry.
A very, very interesting new paper:
The Platonic Representation Hypothesis conjectures that the representation spaces of modern neural networks are converging. We assert the Strong Platonic Representation Hypothesis: the latent universal representation can be learned and harnessed to translate between representation spaces without any encoders or paired data.
Our conjecture is as follows: neural networks trained with the same objective and modality, but with different data and model architectures, converge to a universal latent space such that a translation between their respective representations can learned without any pairwise correspondence.
R. Jha, C. Zhang, V. Shmatikov, and J. X. Morris, “Harnessing the Universal Geometry of Embeddings,” arXiv preprint arXiv:2505.12540, May 2025.