arXiv:2604.00208v2 Announce Type: replace Abstract: Comparing internal representations is a central goal in neuroscience and machine learning, but standard linear alignment metrics (Representational Similarity Analysis, Centered Kernel Alignment, and linear regression) are frequently applied to neural activity coordinates rather than on the underlying features. We show this matters when neural systems operate in superposition, encoding more features than they have neurons via linear compression. Closed-form derivations prove that these metrics depend on the Gram matrices of each system's proje
This paper offers a foundational advancement in understanding neural network mechanics, published as the field of AI interpretation and efficiency is rapidly developing.
Improved methods for comparing internal representations in AI can lead to more efficient, robust, and interpretable models, impacting the development cycle and deployment of advanced AI systems.
The understanding of how neural networks handle superposition and process information is refined, potentially guiding future AI architecture and alignment research.
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New metrics for evaluating neural representations improve model development and debugging.
More sophisticated AI architectures could emerge, particularly in areas requiring higher interpretability or efficiency.
These foundational improvements could accelerate the development of more complex and autonomous AI systems, potentially impacting numerous industries.
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