Harnessing the Universal Geometry of Embeddings

Strong Platonic Representation Hypothesis 및 vec2vec을 소개한 논문.

Abstract

We introduce the first method for translating text embeddings from one vector space to another without any paired data, encoders, or predefined sets of matches. Our unsupervised approach translates any embedding to and from a universal latent representation (i.e., a universal semantic structure conjectured by the Platonic Representation Hypothesis). Our translations achieve high cosine similarity across model pairs with different architectures, parameter counts, and training datasets. The ability to translate unknown embeddings into a different space while preserving their geometry has serious implications for the security of vector databases. An adversary with access only to embedding vectors can extract sensitive information about the underlying documents, sufficient for classification and attribute inference.

arxiv.org/abs/2505.12540

메모

한편, On the Theoretical Limitations of Embedding-Based Retrieval 같은 연구를 생각해보면 Strong Platonic Representation Hypothesis에 따르는 보안 위험은 제한적이겠다. —2025-10-01, AK