跳到正文
arXiv cs.AI· Aravind Mandiga, Guoming Li·· 11 小时前AI 评分31

LLM 生成的证明在程序重构下有多稳定?RefactorProof 对可验证代码生成的压力测试

How Stable Are LLM-Generated Proofs Under Program Refactoring? A Stress Test for Verifiable Code Generation

AI 导读

研究团队提出 RefactorProof,从 185 个 VERINA 任务中派生出 458 个 Lean 认证变体,在规格与证明逐字节固定的前提下检验程序重构后证明是否仍有效。

正文 · 原文

View PDF HTML (experimental)

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