# Vero: Can AI Agents Build Formally Verified Software Repositories?

arXiv: https://arxiv.org/abs/2608.13522 · HTML: https://mlsanity.com/paper/2608.13522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 27 of 27)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021)
- [The Lean mathematical library](https://mlsanity.com/paper/1910.09336/) (2019)
- [Clover: Closed-Loop Verifiable Code Generation](https://mlsanity.com/paper/2310.17807/) (2023)
- [Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models](https://mlsanity.com/paper/2403.04811/) (2024)
- [DafnyBench: A Benchmark for Formal Software Verification](https://mlsanity.com/paper/2406.08467/) (2024)
- [Verus: Verifying Rust Programs using Linear Ghost Types (extended version)](https://mlsanity.com/paper/2303.05491/) (2023)
- [AutoVerus: Automated Proof Generation for Rust Code](https://mlsanity.com/paper/2409.13082/) (2024)
- [LeanAgent: Lifelong Learning for Formal Theorem Proving](https://mlsanity.com/paper/2410.06209/) (2024)
- [VERINA: Benchmarking Verifiable Code Generation](https://mlsanity.com/paper/2505.23135/) (2025)
- [CLEVER: A Curated Benchmark for Formally Verified Code Generation](https://mlsanity.com/paper/2505.13938/) (2025)
- [Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification](https://mlsanity.com/paper/2412.14063/) (2024)
- [A benchmark for vericoding: formally verified program synthesis](https://mlsanity.com/paper/2509.22908/) (2025)
- [Proving the Coding Interview: A Benchmark for Formally Verified Code Generation](https://mlsanity.com/paper/2502.05714/) (2025)
- [miniCodeProps: a Minimal Benchmark for Proving Code Properties](https://mlsanity.com/paper/2406.11915/) (2024)
- [VeruSAGE: A Study of Agent-Based Verification for Rust Systems](https://mlsanity.com/paper/2512.18436/) (2025)
- [AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms](https://mlsanity.com/paper/2602.09464/) (2026)
- [VerifyThisBench: Generating Code, Specifications, and Proofs All at Once](https://mlsanity.com/paper/2505.19271/) (2025)
- [VeriSoftBench: Repository-Scale Formal Verification Benchmarks for Lean](https://mlsanity.com/paper/2602.18307/) (2026)
- [VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally Verifiable Code](https://mlsanity.com/paper/2510.06296/) (2025)
- [Towards Repository-Level Program Verification with Large Language Models](https://mlsanity.com/paper/2509.25197/) (2025)
- [Certified Program Synthesis with a Multi-Modal Verifier](https://mlsanity.com/paper/2604.16584/) (2026)
- [s2n-bignum-bench: A practical benchmark for evaluating low-level code reasoning of LLMs](https://mlsanity.com/paper/2603.14628/) (2026)

## Built on by (most cited first, 2 of 2)

- [Lean Pool: An AI-Maintained Archive of Formalized Mathematics](https://mlsanity.com/paper/2609.25199/) (2026)
- [Cost-Efficient Theorem Proving via Agent Orchestration in Program Verification](https://mlsanity.com/paper/2610.09681/) (2026)

## Abstract (arXiv)

AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an implementation and a machine-checked proof of its specification, offers a stronger path toward trustworthy AI-generated software. Existing benchmarks in this direction either focus on individual functions or only evaluate proof generation with provided implementations. It is still an open question whether agents can make coherent implementation and proof choices across real multi-module codebases. To bridge this gap, we introduce Vero, the first benchmark to evaluate joint implementation and proof synthesis at the repository level. Vero contains 43 multi-module instances sourced from real-world repositories spanning Python, Dafny, Verus, and Coq, and covering diverse domains from cryptographic protocols to distributed systems. Each instance consists of a multi-module Lean 4 repository with predetermined API interfaces, manually curated formal specifications, and reference implementations, supporting both proof-only and code-and-proof evaluation modes. To improve benchmark reliability, Vero also includes an audit mechanism where agents are allowed to formally prove unsatisfiability of provided specification or incorrectness of reference code, which surfaces and corrects latent code and specification errors during curation. We evaluate frontier coding-agent configurations with Lean toolchain access. The strongest agent fully solves only 27 of 43 instances and closes no specifications on the hardest repositories. Vero provides a concrete testbed for measuring progress toward repository-scale verified software synthesis, where current agents still fall short. We release the benchmark, curation pipeline, and evaluation harness at https://github.com/sunblaze-ucb/vero.