We present and test the largest benchmark for vericoding, LLM-generation of formally verified code from formal specifications - in contrast to vibe coding, which generates potentially buggy code from a natural language description. Our benchmark contains 12,504 formal specifications, with 3,029 in Dafny, 2,334 in Verus/Rust and 7,141 in Lean. Of these, 6,174 are new unseen problems. We find vericoding success rates of 27% in Lean, 44% in Verus/Rust and 82% in Dafny using off-the-shelf LLMs. Adding natural-language descriptions does not significantly improve performance. We also find that LLM progress has improved progress on pure Dafny verification from 68% to 96% over the past year. The benchmark and vericoding results are shared at https://github.com/Beneficial-AI-Foundation/vericoding-benchmark
@article{arxiv.2509.22908,
title = {A benchmark for vericoding: formally verified program synthesis},
author = {Sergiu Bursuc and Theodore Ehrenborg and Shaowei Lin and Lacramioara Astefanoaei and Ionel Emilian Chiosa and Jure Kukovec and Alok Singh and Oliver Butterley and Adem Bizid and Quinn Dougherty and Miranda Zhao and Max Tan and Max Tegmark},
journal= {arXiv preprint arXiv:2509.22908},
year = {2025}
}
Comments
25 pages, 1 figure; data available at https://github.com/Beneficial-AI-Foundation/vericoding-benchmark