Harnessing the Universal Geometry of Embeddings
This article provides metadata and access links for a research paper titled 'Harnessing the Universal Geometry of Embeddings'. It serves as a repository entry for academic work in machine learning.
Why it matters
It facilitates access to cutting-edge research in vector embeddings, which are foundational to modern AI and large language models.
Focus to learn more 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 View PDF HTML (experimental) TeX Source 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 export BibTeX citation Loading... BibTeX formatted citation loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer?
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