Predicting the Next State Is Not Enough: JEPA Representations for Lean Theorem Proving

Abstract:Neural theorem provers must both propose tactics and decide which valid successor states to explore. We study whether one-step Lean transitions provide a self-supervised signal for branch ordering. A JEPA-style model predicts latent successor representations and scores only kernel-validated, nonterminal successors generated by a fixed pretrained ByT5 proposer. JEPA achieves higher Top-1 than matched InfoNCE on a same-theorem ranking diagnostic(50.18% versus 31.55%), but averages 282.3 of 987 solved theorems across three seeds versus 308 for proposer ordering, while requiring more tactic checks. In this setting, accurate one-step transition ranking is therefore insufficient as a long-horizon search value. The controlled evaluation separates representation from proposal quality and treats kernel-checked proof completion as the primary endpoint.

Machine Learning (cs.LG)

arXiv:2609.32908 [cs.LG]

(or arXiv:2609.32908v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.32908 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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