arXiv:2602.06205v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces. However, current strategies for mapping these representations are inherently pairwise, scaling quadratically with the number of models and failing to yield a consistent global reference. In this paper, we study the alignment of $M \ge 3$ models. We first adapt Generalized Procrustes Analysis (GPA) to construct a shared orthogonal universe that preserves the internal geometry essential for tasks like
The proliferation of independently developed neural networks has exposed limitations in current pairwise representation alignment techniques, driving a need for more scalable multi-way solutions.
Efficient multi-way representation alignment could significantly enhance the interoperability and integration of AI models, fostering a more unified and powerful AI ecosystem.
The ability to consistently align representations across numerous disparate AI models moves beyond ad-hoc pairwise approaches, enabling more robust meta-learning and knowledge transfer.
- · AI researchers and developers
- · Companies using multiple AI models
- · AI interoperability platforms
- · Foundation model developers
- · Ad-hoc integration solution providers
Increased efficiency in combining and comparing results from multiple independently trained AI models.
Accelerated development of AI systems capable of integrating diverse specialized components seamlessly.
Potential for new AI architectures that leverage globally aligned latent spaces to achieve emergent capabilities.
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Read at arXiv cs.AI