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Researchers prove when JEPA models share a representation of the world
Randall Balestriero and Yann LeCun argue in an Oct. 29 arXiv paper that Joint-Embedding Predictive Architectures trained on matching views of one world converge up to a rotation.
- New headline Was: “Proving and Improving the Platonic Representation Hypothesis with JEPAs”
- Our desk filed its story
- arxiv.org is now reporting this
Brown University and AMI Labs researcher Randall Balestriero and New York University and AMI Labs researcher Yann LeCun posted an Oct. 29 paper on arXiv arguing that Joint-Embedding Predictive Architectures, or JEPAs, trained separately on different modalities can be shown to learn the same representation when they share the same world1.
Huh and colleagues had described the Platonic Representation Hypothesis as an observation that vision and language models grow more similar as they scale1. Balestriero and LeCun write that for practical self-supervised objectives, including JEPAs, there had been no proof of when that holds and no recipe to improve alignment beyond scaling1.
Their argument is that a JEPA with whitened second-order anti-collapse learns the spectral embedding of its positive pairs — inputs it treats as views of each other1. If those pairs relate the same world states across modalities, they say the training problems are the same problem in different coordinates, so the solutions coincide up to a rotation, which they call the Platonic Representation Theorem1.
They say the claim is not limited to images and text and would apply to video, audio or actions whenever modalities observe one world1. They also distinguish “good views,” positives that are two observations of the same scene, from crop-style views used by some image models1.
On experiments, they report that with identical texts and encoders, good views aligned best in most of the comparisons they ran, from 145,000 Flickr captions to texts of 940 million web images1. Trained on texts of 50 million web images, a 33-million-parameter text JEPA scored 0.125 on the Platonic Representation Hypothesis paper’s benchmark, versus 0.034 on Flickr captions, which they say matched Qwen2.5-3B and Qwen2.5-7B1.
On the same corpus, they say a good-view JEPA reached with 21.6 million training samples the alignment that next-token prediction reached with 900 million samples, 42 times more1. Trained on texts of 940 million web images, a 109-million-parameter text JEPA scored 0.184, above every language model they evaluated up to Qwen2.5-72B at 0.154, and above CLIP’s text encoder at 0.124, on two benchmarks and two metrics1.
They write that the 109-million encoder, which read 116 billion text tokens, beat models they describe as 660 times larger trained on over 100 times more data, and that next-token prediction is an inefficient use of scale for alignment1.
They also report captioning images without training a network on image-caption pairs: one linear map with orthonormal rows, fitted on pairs, joining frozen DINOv2 and DINOv3 image encoders to a language model fine-tuned on text alone, reaching CIDEr 0.838 on COCO1. With unpaired images and captions they say their 109-million text JEPA beat zero-shot accuracies reported by Schnaus and colleagues, including a 17-point gain over Qwen3-8B on ImageNet-100 under identical conditions1.
The paper is a preprint and has not been described as peer-reviewed in the arXiv posting1. Independent replication of the scaling and captioning numbers is not in the public record cited here.
