arXiv:2602.14486v2 Announce Type: replace Abstract: The Platonic Representation Hypothesis suggests that representations from neural networks are converging to a common statistical model of reality. We show that the existing metrics used to measure representational similarity are confounded by network scale: increasing model depth or width can systematically inflate representational similarity scores. To correct these effects, we introduce a permutation-based null-calibration framework that transforms any representational similarity metric into a calibrated score with statistical guarantees. W
The paper is published as research into AI interpretability and representational understanding intensifies, driven by the increasing complexity and deployment of large neural networks.
This research provides a critical framework for evaluating neural network representations, potentially leading to more accurate and reliable AI models. Improved understanding of AI's internal workings has implications for safety, trustworthiness, and further development.
Current metrics for representational similarity in AI may be systematically flawed due to network scale; a new calibration framework introduces statistical guarantees for more accurate assessment.
- · AI researchers
- · AI safety institutions
- · Developers of foundational AI models
- · Research relying on uncalibrated representational similarity metrics
- · Organizations deploying AI without robust interpretability measures
More robust and reliable methods for comparing and understanding neural network internal states become standard.
This improved understanding could accelerate progress in AI safety, alignment, and the development of new, more transparent AI architectures.
Enhanced interpretability could enable AI systems to explain their decisions in complex real-world applications, fostering greater public trust and broader adoption.
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Read at arXiv cs.LG