Proving and Improving the Platonic Representation Hypothesis with JEPAs
Abstract
Whether models trained on different modalities converge to the same representation of the world is central to AI. The Platonic Representation Hypothesis (PRH) conjectures that they do as they scale, but without a proof it offers neither a guarantee nor a recipe. We turn the hypothesis into a theorem. First, we prove a \emph{Platonic Representation Theorem}: Joint-Embedding Predictive Architectures (JEPAs) trained separately on any number of modalities learn the same representation up to one rotation whenever their positive pairs are two observations of one scene--\emph{good views}--and their misalignment is bounded otherwise. Second, we show that next-token prediction is a JEPA with \emph{bad views}, parts of a single document, which explains why language models align with vision only through scale. Third, we validate the theory from 145k captions to the texts of 940M web images, and uncover a scaling law of alignment that predicts unseen runs within seed noise. Good views need 42 times fewer training samples than next-token prediction on the same corpus to reach the same alignment, and a 109M-parameter text JEPA aligns with DINOv2 and CLIP's image encoders better than every language model we evaluated, up to 72B parameters, and than CLIP's text encoder: next-token prediction is a remarkably inefficient route to platonic representations. As a showcase, one rotation turns separately trained encoders into an image captioner (CIDEr .838 on COCO). Even that rotation needs no pair: on the latest pair-free benchmark, our text JEPA beats every zero-shot accuracy reported with language models up to 8B parameters, e.g., 64\% against at most 56\% top-5 on ImageNet-100. Positive views, and not scale alone, are thus the lever to build platonic representations.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper proves the Platonic Representation Hypothesis for JEPAs with good views, demonstrating that cross-modal alignment is a property of the positive-pair distribution rather than just scale, and validates this with a 109M model outperforming 72B LLMs in alignment and enabling efficient zero-shot captioning.
Methodology
The paper presents a rigorous theoretical framework for the Platonic Representation Hypothesis (PRH) by proving that Joint-Embedding Predictive Architectures (JEPAs) trained on "good views" (multiple observations of the same scene) converge to the same representation up to rotation. The core novelty lies in distinguishing "good views" (cross-observation positives) from "bad views" (corruptions of a single observation) and formally linking next-token prediction to the latter. The theoretical proofs rely on spectral analysis of the positive-pair kernel, establishing conditions under which cross-modal alignment is guaranteed. The methodology is sound, leveraging established tools from contrastive learning theory (Laplacian eigenmaps, spectral gaps) but applying them in a novel cross-modal context. The extension to show that next-token prediction is a "bad-view" JEPA is a clever and insightful reformulation that explains the inefficiency of LLMs in achieving cross-modal alignment compared to vision models.
Results & evaluation
The experimental validation is extensive and convincing. The authors validate the theory on Flickr30k, COCO, and large-scale web image datasets (Recap-DataComp). Key results include: (1) Good-view JEPAs achieve significantly higher cross-modal alignment (mutual 10-NN) than bad-view JEPAs and next-token prediction on identical data. (2) A 109M-parameter text JEPA trained on good views outperforms language models up to 72B parameters in alignment with DINOv2 and CLIP image encoders. (3) The paper demonstrates a 42x sample efficiency gain for good views over next-token prediction to reach equivalent alignment. (4) A practical application is shown where a single rotation matrix aligns frozen DINOv2 and text JEPA encoders to enable zero-shot image captioning (CIDEr .838 on COCO) and zero-shot classification (64% on ImageNet-100), outperforming larger LLMs. The scaling law derived for alignment is predictive of unseen runs within seed noise, adding significant empirical weight to the theoretical claims.
Reproducibility
The paper provides detailed hyperparameters, dataset splits, and training protocols. The use of standard architectures (ViT, BERT) and objectives (LeJEPA) enhances reproducibility. The code for the rotation fitting and captioning demo is implied to be available, though specific URLs are not present in the text. The scaling law parameters are provided, allowing others to predict alignment for their own setups.
Limitations
The theoretical proofs assume a finite world and injective renderings, which are idealizations. The "good view" definition requires multiple distinct observations of the same scene, which can be data-hungry for images (e.g., video frames) compared to text (multiple captions). The paper acknowledges that image JEPAs trained on good views (video frames) are currently weaker than DINOv2 due to data scale, leaving the image-side scaling of good views as future work. The captioning results rely on a pretrained LLM decoder, so the "zero-shot" claim applies to the alignment/rotation, not the language generation capability itself.
Broader impact
This paper has high potential impact by providing a theoretical recipe for improving cross-modal alignment. It suggests that current practices of scaling LLMs for multimodal tasks are inefficient and that focusing on "good views" (multi-observation positives) is a more effective lever. The demonstration of alignment via simple rotations opens new avenues for lightweight multimodal integration, potentially reducing the need for massive paired datasets or joint training. The insights into why LLMs align poorly with vision models (bad views) are valuable for the design of future multimodal foundation models. The paper proves the Platonic Representation Hypothesis for JEPAs with good views, demonstrating that cross-modal alignment is a property of the positive-pair distribution rather than just scale, and validates this with a 109M model outperforming 72B LLMs in alignment and enabling efficient zero-shot captioning.
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