ProofAug extracts progressively coarser valid proof skeletons from failed LLM proof attempts and fills them with automated theorem provers, improving miniF2F pass rates and sample efficiency in Isabelle and Lean.
The Isabelle ENIGMA
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abstract
We significantly improve the performance of the E automated theorem prover on the Isabelle Sledgehammer problems by combining learning and theorem proving in several ways. In particular, we develop targeted versions of the ENIGMA guidance for the Isabelle problems, targeted versions of neural premise selection, and targeted strategies for E. The methods are trained in several iterations over hundreds of thousands untyped and typed first-order problems extracted from Isabelle. Our final best single-strategy ENIGMA and premise selection system improves the best previous version of E by 25.3% in 15 seconds, outperforming also all other previous ATP and SMT systems.
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cs.LG 1years
2025 1verdicts
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ProofAug: Efficient Neural Theorem Proving via Fine-grained Proof Structure Analysis
ProofAug extracts progressively coarser valid proof skeletons from failed LLM proof attempts and fills them with automated theorem provers, improving miniF2F pass rates and sample efficiency in Isabelle and Lean.