vec2vec learns a fully unpaired translation between six independently-trained text embedding models (gtr, clip, e5, gte, stella, granite), recovering cosine similarity up to 0.92 and top-1 retrieval accuracy up to 100% with no paired training data, and an ablation shows the explicit Gram-matrix-preservation (VSP) loss is causally necessary -- removing it alone collapses top-1 accuracy from 0.91 to 0.00

Jha, Zhang, Shmatikov & Morris (2026) propose the Strong Platonic Representation Hypothesis -- that neural networks trained with the same objective and modality, but different data and architectures, converge to a universal latent space such that a translation between their representations can be learned without any pairwise correspondence -- and test it constructively with vec2vec: input adapters (A1, A2) map each of two embedding spaces into a shared latent, a shared backbone T processes the latent, and output adapters (B1, B2) map back out, trained adversarially (GAN-style discriminators on both the final outputs and the shared latents) with a generator loss combining reconstruction, cycle-consistency, and Vector Space Preservation (VSP) -- an explicit term matching the pairwise inner-product (Gram) matrix of a batch of embeddings before and after translation. Across six independently-trained text embedding models (gtr, 110M/T5/2021; clip, 151M/CLIP/2021; e5, 109M/BERT/2022; gte, 109M/BERT/2023; stella, 109M/BERT/2023; granite, 278M/RoBERTa/2024, multilingual) trained on entirely disjoint, unpaired subsets of Natural Questions text (1 million sequences per side, no shared documents), vec2vec achieves cosine similarity up to 0.92 (stella to gte) and top-1 retrieval accuracy up to 100% (rank as low as 1.00) on a 65536-text held-out evaluation, versus a naive identity-mapping baseline (near-zero cosine similarity, 0% top-1 on cross-backbone pairs) and an optimal-assignment baseline using Hungarian/Sinkhorn/ Gromov-Wasserstein solvers (0% top-1, rank near-random ~4096-4209 on cross-backbone pairs) -- both baselines perform at essentially chance on cross-backbone pairs while vec2vec does not. A component ablation (gte to gtr) directly ties this success to the explicit geometry- preservation term rather than to model capacity generally: the full method reaches cosine 0.75/top-1 0.91/rank 2.64, but removing only the VSP loss drops this to cosine 0.58/top-1 0.00/rank 4196.64 -- a collapse to nearly the same failure regime as removing the latent GAN entirely (cosine 0.49/top-1 0.00/rank 3897.09) or the cycle-consistency loss (cosine 0.50/top-1 0.00/rank 3941.36). The translation further extends, with reduced but still far-above-baseline performance, to out-of-distribution domains (TweetTopic, MIMIC-III clinical text) and to CLIP's multimodal text tower, and the translated embeddings retain enough source information to support downstream attribute-inference (top-1 accuracy up to 0.90 on a 10-way clinical-code task) and document-reconstruction attacks (LLM-judged information extraction for up to 80% of a 50-email test set) -- so the same convergence that enables unsupervised translation is also demonstrated as a concrete privacy vulnerability of embedding-based systems.