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arXiv · 2510.25427

RLMEval: Evaluating Research-Level Neural Theorem Proving

Auguste Poiroux·Antoine Bosselut·Viktor Kunčak
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Abstract

Despite impressive results on curated benchmarks, the practical impact of large language models (LLMs) on research-level neural theorem proving and proof autoformalization is still limited. We introduce RLMEval, an evaluation suite for these tasks, focusing on research-level mathematics from real-world Lean formalization projects. RLMEval targets the evaluation of neural theorem proving and proof autoformalization on challenging research-level theorems by leveraging real Lean Blueprint formalization projects. Our evaluation of state-of-the-art models on RLMEval, comprising 613 theorems from 6 Lean projects, reveals a significant gap: progress on existing benchmarks does not readily translate to these more realistic settings, with the best model achieving only a 10.3 % pass rate. RLMEval provides a new, challenging benchmark designed to guide and accelerate progress in automated reasoning for formal mathematics.

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Auguste Poiroux, Antoine Bosselut, Viktor Kunčak. 2025-10-29. RLMEval: Evaluating Research-Level Neural Theorem Proving. https://arxiv.org/abs/2510.25427

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