arXiv:2606.32007v1 Announce Type: new Abstract: We study agentic code generation in Dafny, where a model must generate both executable code and the proof artifacts for verification. We present AxDafny, a verifier-guided repair framework that iteratively generates implementations, invariants, assertions, and termination arguments. We also introduce LiveCodeBench-Pro-Dafny (LCB-Pro-Dafny), a benchmark of 250 competition-style programming problems translated into Dafny with formal specifications and a verifier-based evaluation harness. On LCB-Pro-Dafny, AxDafny substantially improves verification
The increased sophistication of large language models and the push for more reliable, verifiable code in critical applications drive this advancement in agentic code generation.
This development signifies a leap towards AI systems capable of not only generating complex code but also proving its correctness, which is crucial for safety-critical and high-assurance software.
The paradigm shifts from human-driven verification of AI-generated code to AI-driven verification during the generation process, enhancing reliability and reducing human oversight needs.
- · Software Development industry
- · High-assurance systems developers
- · AI agents researchers
- · Formal verification tools vendors
- · Manual code verification services
- · Developers relying solely on traditional testing
The adoption of verifiable AI-generated code accelerates development cycles for complex software projects.
This could lead to increased automation in software engineering, potentially displacing some human coding and verification roles.
The ability to generate provably correct code could enable entirely new categories of autonomous and safety-critical AI systems, expanding the scope of AI applications.
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Read at arXiv cs.AI