arXiv:2605.31100v1 Announce Type: new Abstract: 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 re
The proliferation of various foundational models and embedding techniques necessitates robust methods for interoperability and data integration without direct data sharing.
A strategic reader should care as this advancement offers a robust new method for connecting disparate data silos and models, enhancing data utility across different AI systems and organizations.
The ability to link vector embeddings from different models using geometric consistency allows for deeper cross-platform intelligence and potentially enables new forms of collaborative AI without centralizing raw data.
- · AI developers (especially in data integration)
- · Cloud providers offering AI services
- · Organizations with diverse data and model ecosystems
- · Data scientists and researchers
- · Companies reliant on data exclusivity as a competitive barrier
- · Inefficient manual data mapping services
Improved interoperability between AI systems and datasets.
Accelerated development of composite AI applications drawing insights from previously incompatible sources.
Potential for new data marketplaces or AI ecosystems built on secure, linked vector representations rather than raw data exchanges.
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Read at arXiv cs.AI