Computer Science > Machine Learning

arXiv:2505.12540 (cs) [Submitted on 18 May 2025 (v1), last revised 26 Jan 2026 (this version, v4)]

Title:Harnessing the Universal Geometry of Embeddings

Authors:Rishi Jha, Collin Zhang, Vitaly Shmatikov, John X. Morris View a PDF of the paper titled Harnessing the Universal Geometry of Embeddings, by Rishi Jha and 3 other authors View PDF HTML (experimental)

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.

Subjects: Machine Learning (cs.LG) Cite as: arXiv:2505.12540 [cs.LG]   (or arXiv:2505.12540v4 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2505.12540 arXiv-issued DOI via DataCite

Submission history

From: Rishi Jha [view email]
[v1] Sun, 18 May 2025 20:37:07 UTC (3,179 KB)
[v2] Tue, 20 May 2025 15:38:41 UTC (3,180 KB)
[v3] Wed, 25 Jun 2025 21:04:02 UTC (2,407 KB)
[v4] Mon, 26 Jan 2026 14:47:13 UTC (2,424 KB)
Full-text links:

Access Paper:

View a PDF of the paper titled Harnessing the Universal Geometry of Embeddings, by Rishi Jha and 3 other authors
• HTML (experimental)

view license

Current browse context:

cs.LG < prev   |   next >
new | recent | 2025-05 Change to browse by: cs

References & Citations

• NASA ADS
• Google Scholar
• Semantic Scholar

Loading...

BibTeX formatted citation

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos

Demos

Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers

Recommenders and Search Tools

Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?)

• Author
• Venue
• Institution
• Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)