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Balestriero and LeCun formally prove the Platonic Representation Hypothesis for JEPAs: models trained separately on any modality learn the same representation up to rotation, provided they use good positive pairs. The result reframes next-token prediction as a JEPA with structurally bad views, explains the slow alignment of language models with vision, and shows a 109M text JEPA outperforms every LLM tested up to 72B parameters on cross-modal tasks.
Read more →A new paper from McCoy, Soulos, Linzen, and Smolensky shows that neural network representations implicitly realize symbolic structures — precisely enough to replace the network's entire representation process with a closed-form equation, and to intervene on LLM behavior by directly editing the identified structures.
Read more →Fourteen researchers across Berkeley, MIT, Harvard, and EPFL published a 41-page manifesto arguing that a scientific theory of deep learning is not just desirable but already forming. They call it "learning mechanics" and point to five converging research threads — solvable models, tractable limits, empirical laws, hyperparameter theories, and universal behaviors — that together look something like what statistical mechanics looked like before it became statistical mechanics.
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