Vector Linking via Cross-Model Local Isometric Consistency

Computer Science > Artificial Intelligence [Submitted on 29 May 2026] Vector Linking via Cross-Model Local Isometric Consistency Ziying Chen, Yang Cao, He Sun, Beining Yang, Tianjian Yang We study Vector Linking: given two embedding clouds produced by different black-box encoders over partially overlapping datasets, recover cross-model object correspondences using only vectors. Empirically and theoretically, we show that independently trained contrastive encoders exhibit local geometric consistency: short-range distances are approximately preserved up to a scale factor, while long-range distances are not due to model-specific distortion. Building on this, we propose an iterative, reference-based geometric embedding hashing that recovers vector links from a tiny seed set of paired anchors. It represents each vector by distances to sampled paired anchors, proposes candidate links via hash-space matching, and aggregates evidence across views in a Beta-Bernoulli posterior to bootstrap high-confidence links as new anchors. Experiments across multiple benchmarks and embedding model pairs demonstrate accurate and robust linking under varying overlap, seed budgets, and out-of-domain anchors, with applications to vector database integration and cross-model clustering. Code is available at this https URL. Comments: Accepted at ICML 2026 Subjects: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR) Cite as: arXiv:2605.31100 [cs.AI]   (or arXiv:2605.31100v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.31100 Focus to learn more Submission history From: Ziying Chen [view email] [v1] Fri, 29 May 2026 10:12:52 UTC (767 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.DB cs.IR References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)