arXiv:2602.09464v2 Announce Type: replace-cross Abstract: Vericoding refers to the generation of formally verified code from rigorous specifications. Recent AI models show promise in vericoding, but a unified methodology for cross-paradigm evaluation is lacking. Existing benchmarks test only individual languages/tools (e.g., Dafny, Verus, and Lean) and each covers very different tasks, so the performance numbers are not directly comparable. We address this gap with AlgoVeri, a benchmark that evaluates vericoding of $77$ classical algorithms in Dafny, Verus, and Lean. By enforcing identical fun
The proliferation of AI code generation tools has created an urgent need for robust verification methods, prompting researchers to develop unified benchmarks for evaluating verified code generation.
This benchmark is crucial for advancing the reliability and trustworthiness of AI-generated code, especially in critical applications where formal verification is non-negotiable.
The ability to directly compare AI models across different verification frameworks and languages based on a standardized benchmark will accelerate the development of more reliable vericoding AI.
- · AI developers focused on code reliability
- · High-assurance software industries
- · Academic researchers in formal verification
- · Companies relying on unverified AI-generated code
- · Developers unable to integrate formal verification tools
Improved benchmarks lead to more capable AI models for generating formally verified code.
Increased adoption of AI in safety-critical software development due to higher verification confidence.
Reduced incidence of software bugs and vulnerabilities in complex systems, enhancing digital infrastructure security.
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Read at arXiv cs.AI