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

arXiv cs.AI 约 12 分钟读完

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 compi…

Authors: Aravind Mandiga , Guoming Li

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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)

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arXiv:2610.36328 [cs.AI]

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arXiv:2610.36328v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36328

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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)

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原文标题 How Stable Are LLM-Generated Proofs Under Program Refactoring? A Stress Test for Verifiable Code Generation
引用来源 arXiv cs.AI
本地存档 2026-10-28 16:15
引用格式 Aravind Mandiga, Guoming Li. How Stable Are LLM-Generated Proofs Under Program Refactoring? A Stress Test for Verifiable Code Generation. 2026-10-28. https://www.ai007.site/news/52534

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