arXiv:2602. 14486v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that representations from neural networks are converging to a common statistical model of reality.
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The paper investigates the Platonic Representation Hypothesis, which posits that more capable models converge toward shared representations. By distinguishing relational structure (which samples are related) from metric geometry (quantitative relations like distances), the authors develop a controlled $2 imes2$ framework to evaluate both aspects at local and global scales. Their findings show that relational structure consistently converges across vision‑language and video‑text models, while metric geometry converges much more weakly, a pattern that persists even when using a Riemannian metric approximation.
arXiv:2608.20682v1 Announce Type: new Abstract: This paper formalizes and systematically characterizes Aristotelian Manifolds, a generalized structural framework built upon the Platonic Representatio...
arXiv:2607. 17800v1 Announce Type: new Abstract: Representation is a central concept in modern machine learning, where it usually refers to internal encodings that support learning and generalization.
arXiv:2604. 18572v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.
arXiv:2606. 23885v1 Announce Type: cross Abstract: Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal representations toward those of an external vision encoder.
arXiv:2602. 06205v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces.