APE-Bench: Evaluating Automated Proof Engineering for Formal Math Libraries
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Abstract (arXiv)
While frontier formal mathematics systems now routinely develop repository-scale proof engineering artifacts requiring multi-file coordination and semantic correctness beyond compilation, existing evaluation benchmarks remain focused on isolated theorem proving. We introduce Automated Proof Engineering (APE), the first systematic framework for evaluating repository-scale proof engineering through dual verification that validates both syntactic compilation and semantic requirement satisfaction in pinned library environments. We present a complete infrastructure comprising APE-Bench, which automatically extracts proof engineering tasks from real library commit histories, and APE-Harness, a unified execution framework based on task contract abstraction. This contract-based design enables standardized evaluation across diverse formal mathematics tasks and fair systematic comparison of different agent implementations (including our APE-Agent reference scaffold alongside Claude Code and Codex CLI) on identical task specifications. We demonstrate the framework's effectiveness through comprehensive evaluation. All code and benchmark dataset are released as open-source at https://github.com/xinhjBrant/APE-Bench.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 14 references that are in the map. First posted on arXiv 27 Apr 2025.
Builds on
6 of 14 references.
References · 6
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
- 2019The Lean mathematical librarycited by 127
- 2024miniCTX: Neural Theorem Proving with (Long-)Contextscited by 35
- 2020QED at Large: A Survey of Engineering of Formally Verified Softwarecited by 13
- 2020Proof Repair across Type Equivalencescited by 8
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Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2025Autoformalization in the Era of Large Language Models: A Surveycited by 19
- 2026Neural Theorem Proving for Verification Conditions: A Real-World Benchmarkcited by 4
- 2026Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verificationcited by 4
- 2025KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignmentcited by 1
- 2026ITPEval: Benchmarking Formal Translation Across Interactive Theorem Proverscited by 1
- 2026ProofJudge: Tool-Grounded LLM Evaluation of Formal Proof Quality in Mathlibcited by 0
- 2026ProofJudge: Tool-Grounded LLM Evaluation of Formal Proof Quality in Mathlib20 Aug 2026
- 2026ITPEval: Benchmarking Formal Translation Across Interactive Theorem Provers7 Jul 2026
- 2026Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification29 Jan 2026
- 2026Neural Theorem Proving for Verification Conditions: A Real-World Benchmark26 Jan 2026
- 2025KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment11 Jul 2025
- 2025Autoformalization in the Era of Large Language Models: A Survey29 May 2025