A new paper from Yann LeCun and Brown University's Randall Balestriero claims a 109-million-parameter text model aligns with image models more precisely than language models 660 times its size. The result arrives weeks after LeCun left Meta to build a startup around the same idea.
The paper is called "Proving and Improving the Platonic Representation Hypothesis with JEPAs," posted to arXiv on October 29 as 2610.39302. Its central claim is specific enough to check. A 109-million-parameter text model, trained as a joint-embedding predictive architecture, produces representations that line up with DINOv2 and CLIP's image embeddings better than any language model the authors tested. That includes ones running up to 72 billion parameters. Against Meta's DINOv2 ViT-B, the small model scored 0.184 on their alignment metric. Qwen2.5-72B scored 0.154.
That's not a marginal win dressed up as news. It inverts a premise a lot of AI infrastructure spending currently rests on: that bigger language models, trained on more text, naturally converge toward the same understanding of the world that vision models learn from images. The paper by Balestriero, an assistant professor at Brown, and LeCun argues that convergence has almost nothing to do with scale and almost everything to do with what the model is shown during training.
The paper's framing turns on a distinction between what it calls good views and bad views. A good view is two different observations of the same underlying scene, say, a photo and a caption describing it, or two camera angles on the same object. A JEPA trained on good views learns to predict one view's embedding from the other's. The paper proves, not just observes, that doing this on any two modalities converges to the same representation up to a single rotation. That holds whenever the training signal qualifies as a good view.
Next-token prediction, the mechanism behind every mainstream large language model, doesn't qualify. Why not? Predicting the next word from the words before it is a JEPA trained on bad views: two slices of the same document rather than two observations of the same reality. The paper's argument is that this is precisely why LLMs only start resembling vision models as they scale into the tens of billions of parameters. Brute scale is a workaround for a training signal that was never structured to align with other modalities in the first place.
Yann LeCun calls Dario Amodei deluded and crazy over AI cybersecurity claims Yann LeCun called Anthropic CEO Dario Amodei "completely deluded" and "crazy" in a Fortune interview, arguing AI labs don't understand cybersecurity well enough to justify their doomsday warnings. The jab lands days after Anthropic's own report on Chinese AI model GLM-5.3's hacking capabilities. - Yann LeCun criticizes Dario Amodei AI security views - Anthropic CEO cybersecurity claims called completely deluded
The efficiency number attached to this is the one driving the attention on Reddit's r/LocalLLaMA, where the paper picked up strong engagement this week. Good views need 42 times fewer training samples than next-token prediction to reach equivalent cross-modal alignment. A 660x smaller model, trained on a fraction of the data, is outperforming 72B-parameter LLMs. That's no tuning trick. It's what the authors say happens when you fix the structure of the learning problem instead of throwing more parameters and more tokens at a flawed one.
Frankly, that's an uncomfortable number. The industry has spent the past two years treating parameter count and training corpus size as the main levers worth pulling. Hyperscalers have poured hundreds of billions into GPU clusters on the bet that scaling the current recipe keeps paying off. Now a paper shows a fix at the training-objective level beating that bet by two orders of magnitude on data efficiency, with real numbers attached. A result like that gets contested loudly if it's wrong and ignored quietly if it's right.
LeCun's own bet just got a paper to back it #
The timing matters. LeCun left Meta after twelve years as its chief AI scientist to launch AMI Labs. It's a Paris-based startup built around world models and the V-JEPA architecture rather than large language models, according to reporting from Reuters and other outlets covering the move. AMI Labs raised over $1 billion in funding before the company had shipped a product, with former Nabla CEO Alexandre LeBrun running the company day to day while LeCun is executive chairman. This paper is the closest thing yet to a technical justification for betting that much money on an approach the rest of the industry has largely passed over.
It's worth saying plainly that one paper is not a verdict. The empirical case currently rests on the authors' own choice of alignment metric and their own benchmark setup, and no independent group has replicated the 109M-versus-72B result yet. Qwen2.5-72B is a real, widely used model, not a strawman, but it's also one data point against one metric. Extraordinary claims about beating giant models with a fraction of the parameters have a way of looking shakier once other labs run the comparison with their own tools.
Still, the theoretical half of the paper doesn't depend on anyone's benchmark holding up. A proof that JEPAs trained separately on different modalities converge to the same representation, given the right kind of training pairs, is a mathematical claim independent of which specific numbers you plug in. If that proof holds, it gives LeCun's years of public skepticism about next-token prediction something it previously lacked. That something is a formal argument for why scaling autoregressive LLMs was always going to be the harder, less efficient road to machines that understand images and language the same way.
Also read: Heretic 2.0 makes stripping AI safety guardrails even easier to automate • Obscure neocloud lenders, not Nvidia, are where the AI bubble could burst first • Small open models match Fable 5 on coding, but the win is a myth
This article is posted in AI News, check it out for more related stories.
Join the discussion #
Open in the community → Almost there. Sign in and your reply posts straight away.