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 https://github.com/DBgroup-Edinburgh/VecLinking.

翻译：我们研究向量链接问题：给定由不同黑盒编码器在部分重叠数据集上生成的两个嵌入点云，仅利用向量恢复跨模型对象对应关系。通过实验和理论分析，我们证明独立训练的对比编码器表现出局部几何一致性：短距离近似保持（仅相差一个尺度因子），而长距离因模型特异性畸变而不一致。基于此，我们提出一种迭代式、基于参考集的几何嵌入哈希方法，通过少量种子配对锚点恢复向量链接。该方法将每个向量表示为与采样配对锚点的距离，通过哈希空间匹配生成候选链接，并利用贝塔-伯努利后验分布跨视图聚合证据，以自举方式生成高置信度链接作为新锚点。在多个基准测试和嵌入模型对上的实验表明，该方法在不同重叠度、种子预算及域外锚点条件下均能实现准确且鲁棒的链接，可应用于向量数据库集成与跨模型聚类。代码开源于 https://github.com/DBgroup-Edinburgh/VecLinking。