RefactorProof:重构下 LLM 生成证明的稳定性压力测试
How Stable Are LLM-Generated Proofs Under Program Refactoring? A Stress Test for Verifiable Code Generation
AI 导读
一项研究提出 RefactorProof 基准,用行为保持的程序重构检验可验证代码生成中证明的稳定性,从 185 个 VERINA 任务派生出 458 个 Lean 认证变体。
正文 · 原文
Abstract:Benchmarks for verifiable code generation usually test a proof against one fixed implementation. We ask whether the same proof remains valid after a program refactoring that preserves behavior. We introduce RefactorProof, which derives 458 Lean-certified variants from the 185 VERINA tasks that compile in our pinned environment while holding the specification and evaluated proof fixed byte-for-byte. VERINA supplies reference proofs for 46 tasks, all from its basic split; the primary model-versus-reference comparisons further match on 21-25 short, non-recursive tasks. Structural refactorings expose substantial brittleness across proof sources: task-normalized structural PSR is 4.3% for the supplied references and 13.3-26.8% for the specialized provers. On the matched tasks, specialized prover proofs nevertheless have higher overall unchanged-proof survival than the supplied reference artifacts: +15.6 points for DeepSeek-Prover-V2-7B, +18.8 for Goedel-Prover-V2-32B, and +20.3 for Goedel-Prover-V2-8B; all three 95% CIs exclude zero. The ordering persists after excluding universally survived T1 variants, under family-balanced aggregation, and under alternative deployable valid-proof selection rules. However, unchanged-proof failure is not the same as high repair cost: predefined one-identifier edits repair 51/54 eligible reference structural failures and 58/85 eligible prover failures. RefactorProof therefore measures unchanged-proof stability under program refactoring, not the cost of maintaining a proof after it breaks.
| Comments: | Accepted at the NeurIPS 2026 Workshop on AI for Verifiable Coding |
| Subjects: | Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Software Engineering (cs.SE) |
| Cite as: | arXiv:2610.36328 [cs.AI] |
| (or arXiv:2610.36328v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36328 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Aravind Mandiga [view email]
[v1]
Mon, 26 Oct 2026 21:56:03 UTC (1,473 KB)
来源:arXiv cs.AI · arxiv.org