Compiler-Guided Adaptive Proof Search with Cross-Model Synergy on Context-Dependent Theorem Proving

2026-06-04

AI Summary

本文提出了一种编译器引导的证明搜索框架，通过双模型生成和停滞触发重采样来探索多样化的起点，并利用编译器基础的配对比较来优化有希望的证明状态。

Abstract

Theorem proving in real-world Lean 4 projects is challenging because proofs often depend on project-specific context. While iterative refinement can use compiler errors to repair failed proofs, reusing failed attempts requires careful search control: some proofs provide better starting points than others, and later revisions may degrade a partially correct proof. We propose a compiler-guided proof search framework that balances exploration and exploitation. It explores diverse starting points through dual-model generation and stagnation-triggered resampling, while exploiting promising proof states through current-best refinement guided by compiler-grounded pairwise comparison. Experiments on seven real-world Lean 4 projects from miniCTX-v2 show that our method achieves a better effectiveness--efficiency tradeoff than pass@k baselines. Within the pass@32 budget, our method improves average pass rate by 12.8 percentage points while reducing LLM calls by 21.9%.

Problem

• Theorem Proving
• Context-Dependent
• Compiler Errors
• Iterative Refinement
• Proof Search

Innovation

• compiler-guided proof search
• dual-model generation
• stagnation-triggered resampling
• current-best refinement
• context-dependent theorem proving