Revisiting the Platonic Representation Hypothesis: An Aristotelian View
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. We revisit the Platonic Representation Hypothesis with our calibration framework, which reveals a nuanced picture: the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity, but not local distances, retains significant agreement across different modalities. Based on these findings, we propose the Aristotelian Representation Hypothesis: representations in neural networks are converging to shared local neighborhood relationships.
69954c908d17d1ee8c10ecc4
Fabian Gröger
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2026-02-18T09:07:56.112Z
616a7abc82a777e9333a1567
https://cdn-avatars.huggingface.co/v1/production/uploads/1634368720319-616a7abc82a777e9333a1567.png
Fabian Gröger
FabianGroeger
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FabianGroeger
69954c908d17d1ee8c10ecc5
Shuo Wen
69954c908d17d1ee8c10ecc6
Maria Brbić
Revisiting the Platonic Representation Hypothesis: An Aristotelian View
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. We revisit the Platonic Representation Hypothesis with our calibration framework, which reveals a nuanced picture: the apparent convergence reported by global spectral measures largely disappears after calibration, while local neighborhood similarity, but not local distances, retains significant agreement across different modalities. Based on these findings, we propose the Aristotelian Representation Hypothesis: representations in neural networks are converging to shared local neighborhood relationships.
Network representations converge toward shared local neighborhood relationships rather than global statistical models, as revealed by calibrated similarity metrics.
representational similarity neural networks spectral measures neighborhood similarity permutation-based null-calibration framework Platonic Representation Hypothesis Aristotelian Representation Hypothesis