arXiv:2605.30596v1 Announce Type: new Abstract: Independently trained neural models typically converge to incompatible latent representations, creating a fundamental barrier to highly modular AI systems. While Relative Representations (RR) address this by mapping absolute coordinates to a shared space defined by similarities to common anchor points, traditional implementations rely on randomly sampled anchors and cosine similarity, which frequently fail to capture the anisotropic geometries of modern architectures like Transformers. In this work, we propose a robust framework for cross-model c
The proliferation of various AI models necessitates new methods for achieving modularity and interoperability, which conventional representations struggle to provide.
This research addresses a fundamental barrier to creating highly modular AI systems, promising to unlock new capabilities and efficiencies in complex AI deployments.
The ability to more effectively integrate and share latent representations between different AI models will reduce silos and improve the performance of composite AI systems.
- · AI platform developers
- · Enterprises deploying multi-modal AI
- · AI researchers
- · Developers of proprietary, siloed AI systems
- · Legacy AI integration services
Improved interoperability between diverse AI models becomes more feasible, accelerating the development of more complex AI applications.
The cost and complexity of integrating different AI components decrease, leading to broader adoption of modular AI architectures.
This could foster new classes of AI systems that combine specialized models in novel ways, potentially enabling emergent capabilities beyond what single large models can achieve.
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Read at arXiv cs.LG