CAPRI: Contract-Aware Proof Repair for Isabelle
Score
77提出编辑合同与审计链结合的新工作流，补充了现有 LLM 证明修复方法缺乏权限控制的研究空白。
实验设计严谨，包含对照条件、重复运行与统计检验，结果呈现完整且讨论充分。
为 Isabelle 用户和形式化验证工程师提供了可落地的安全修复工具与评估基准，具有直接工程指导意义。
论文紧跟 LLM 自动证明与 AI 安全对齐的热点，发布于 arXiv 最新一期，时效性强。
来自 arXiv 学术预印本，作者提供详细实验数据与统计检验，信源可信度较高。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper is the first to introduce a contract-aware proof repair workflow that integrates auditing and edit authorization into LLM-based proof repair, showing strong novelty and engineering value.
AI commentary
「What stands out most is the separation of 'what is allowed to change' from 'whether the change is correct' — a design that is closer to real engineering needs than merely optimizing repair success rates. For Isabelle users and AI code generation developers, CAPRI's auditing mechanism and restricted interface approach offer directly relevant insights.」
Quick overview
CAPRI presents a contract-aware proof repair workflow for Isabelle, where Isabelle checks the proof and an independent checker enforces a machine-readable edit contract to ensure large language models only modify developer-authorized content. The system retains prompts, proposals, candidate repositories, diagnostics, verdicts, and hashes for audit. The study evaluates five workflows on twelve failed proofs from four developments, with three replicates per task and condition, totaling 180 runs and 138 valid repairs. A proof-body-only interface achieved 29/36 valid repairs with zero contract violations, compared to 31/36 for the corresponding full-theory workflow. One-shot repair produced 22/36, while a later prospectively frozen iterative workflow produced 32/36. The results indicate that restricting edits to the proof body provides a favorable balance of effectiveness and safety.
- CAPRI uses an independent checker to enforce a machine-readable edit contract, preventing LLMs from modifying protected text.
- Of 144 terminal candidates accepted by Isabelle, six had modified protected text, all arising from iterative workflows that could edit a complete theory.
- A proof-body-only interface produced 29/36 valid repairs with no contract violations, outperforming the full-theory workflow in safety under comparable settings.
- One-shot repair achieved 22/36, and a prospectively frozen iterative workflow achieved 32/36; the Sol configuration with matched demonstrations reached 33/36, but the difference from the frozen OpenAI Responses condition was not statistically significant.
Full text
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