New paper debunks Platonic Representation Hypothesis, proposes Aristotelian alternative
burny_tech · x · 2026-10-20
A new paper challenges the Platonic Representation Hypothesis — the idea that neural nets across modalities converge to the same representation at scale. The authors show common representational similarity metrics are confounded by network width and depth, and propose a permutation-based null calibration. After correction, global convergence largely disappears while local neighborhoods persist. They propose the Aristotelian Representation Hypothesis: nets trained with different objectives, data and modalities converge to shared local neighborhood relationships, not a shared global representation.
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