LeCun and Balestriero turn the Platonic Representation Hypothesis into a theorem: a 109M text JEPA aligns with vision models better than 72B LLMs
On October 29, Randall Balestriero and Yann LeCun posted "Proving and Improving the Platonic Representation Hypothesis with JEPAs" (arXiv 2610.39302), affiliated with Brown University, NYU and AMI Labs. The paper proves that JEPAs trained separately on different modalities learn the same representation up to one rotation whenever their positive pairs are two observations of one scene ("good views"), and argues that next-token prediction is a JEPA with "bad views", which is why language models only align with vision through scale. The authors report that good views need 42x fewer training samples than next-token prediction to reach the same alignment, and that a 109M text JEPA scores 0.184 alignment with DINOv2 versus 0.154 for Qwen2.5-72B (author-reported). Code and a live demo are public; Balestriero's announcement post has about 1,054 likes and 72K views.
Key Takeaways
- Paper: arXiv 2610.39302, submitted 2026-10-29 by Randall Balestriero and Yann LeCun (Brown University / NYU / AMI Labs)
- On the PRH benchmark, the 109M text JEPA scores 0.184 alignment with DINOv2, versus 0.154 for Qwen2.5-72B and 0.151 for Llama-3-70B; with the CLIP image encoder it reaches 0.23 against a previous best of 0.21 (author-reported)
- Good views reach the same alignment with 42x fewer training samples than next-token prediction; the authors pre-registered predictions from their alignment scaling law, trained 85 new runs, and the law was as accurate as rerunning with another seed
- A single rotation turns separately trained encoders into an image captioner with CIDEr 0.838 on COCO; on a pair-free benchmark it hits 64% top-5 on ImageNet-100 versus at most 56% for language models up to 8B
- Code (galilai-group/prh-proof) and a live demo are public; Balestriero's announcement post has about 1,054 likes and 180 reposts
