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.
By Junwon You, Mihyun Jang, Sangwoo Mo, Jae-Hun Jung
arXiv:2608.20682v1 Announce Type: new
Abstract: This paper formalizes and systematically characterizes Aristotelian Manifolds, a generalized structural framework built upon the Platonic Representatio...
By Michael Karnes, Alper Yilmaz
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.
By Gilad Landau, Aviv Keren
arXiv:2604. 18572v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.
By A. Sophia Koepke, Daniil Zverev, Shiry Ginosar, Alexei A. Efros
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.
By Davide Caffagni, Alberto Compagnoni, Federico Melis, Sara Sarto, Pier Luigi Dovesi, Mark Granroth-Wilding, Marcella Cornia, Lorenzo Baraldi
arXiv:2602. 06205v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces.
By Akshit Achara, Tatiana Gaintseva, Mateo Mahaut, Pritish Chakraborty, Viktor Stenby Johansson, Melih Barsbey, Emanuele Rodol\`a, Donato Crisostomi
The Platonic Representation Hypothesis (PRH) claims that independently trained models converge on a shared statistical model of reality, yet recent work finds only weak pointwise similarity between mo...
arXiv:2608.23720v1 Announce Type: new
Abstract: World models have demonstrated significant potential for perceiving and simulating complex environments. Despite their strong performance, the fundamen...
By Wenhow Li (The Hong Kong University of Science and Technology), Chengwei MA (The Hong Kong University of Science and Technology), Hui Xiong (The Hong Kong University of Science and Technology), Ying-Cong Chen (The Hong Kong University of Science and Technology), Lei Zhang (The Hong Kong University of Science and Technology)
arXiv:2608.23182v1 Announce Type: cross
Abstract: We present a comparative study of label-free metrics for assessing the quality of representations in deep neural networks to understand their reliabi...
By Daniel Richards Arputharaj, Daniel J\"onsson, Gabriel Eilertsen
arXiv:2609.39078v1 Announce Type: new
Abstract: Representations are routinely used across machine learning, psychology, and neuroscience to draw inferences about the computations of biological and ar...
By Marvin Theiss, Lukas Braun, Andrew M. Saxe, Erin Grant
arXiv:2609.38998v1 Announce Type: new
Abstract: A foundational principle of connectionism is that perception, action, and cognition emerge from parallel computations among simple, interconnected unit...
By Lukas Braun, Erin Grant, Andrew M. Saxe
arXiv:2609.24209v1 Announce Type: new
Abstract: The Platonic Representation Hypothesis (PRH) claims that independently trained models converge on a shared statistical model of reality, yet recent wor...
By Chenming Shang, Yujin Tang, Jun Jie Ou Yang, Ruize Xu, Adam Breuer, Nikhil Singh