arXiv:2601.09173v5 Announce Type: replace Abstract: Representational similarity analysis and related methods compare the internal geometries of neural networks, but they measure only alignment between spaces, leaving a blind spot -- whether a representation's structure is reliably recoverable, not merely similar. We introduce geometric stability, a distinct axis, and \textit{Shesha}, a metric that quantifies it from a single representation by correlating dissimilarity matrices built from complementary random halves of the feature dimensions. Unlike CKA and Procrustes distance, Shesha is provab
This paper introduces a novel metric ('Shesha') addressing a known blind spot in representational similarity analysis, offering a new way to evaluate AI model robustness and interpretability.
A strategic reader should care because improved understanding and quantification of AI representation stability can lead to more reliable, auditable, and transferable AI systems, impacting critical applications.
The ability to quantify 'geometric stability' provides a distinct axis for evaluating neural network representations, offering a more complete picture of model quality beyond mere alignment.
- · AI researchers
- · ML engineers
- · AI safety auditors
- · Companies developing critical AI systems
- · Developers of unstable AI models
- · Companies relying solely on traditional similarity metrics
AI models will be evaluated with a new dimension, leading to more robust and less 'brittle' systems.
This improved understanding contributes to faster development of more reliable AI agents and complex autonomous systems.
Enhanced AI stability could accelerate the deployment of AI in safety-critical sectors, potentially shifting economic and defense capabilities.
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Read at arXiv cs.LG