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.
Premise Selection for Theorem Proving by Deep Graph Embedding
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture. We represent a higher-order logic formula as a graph that is invariant to variable renaming but still fully preserves syntactic and semantic information. We then embed the graph into a vector via a novel embedding method that preserves the information of edge ordering. Our approach achieves state-of-the-art results on the HolStep dataset, improving the classification accuracy from 83% to 90.3%.
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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.