AxDafny achieves 92.7% verification success on DafnyBench (6.5 points above prior proof-hint baselines) via verifier-guided repair and introduces the LCB-Pro-Dafny benchmark of 250 problems.
arXiv preprint arXiv:2509.23061 , year =
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Forge pipeline combines LLM code generation with MDE transformations to produce verifiable artifacts in Dafny, CSP, and Isabelle, iterating on failures to generate standards-relevant evidence for Java code.
LLMs can generate natural language specs and perform compositional verification to help prevent vulnerable code from being produced by AI models.
BRIDGE improves Lean executable correctness up to 1.5x and sample efficiency roughly 2x over direct prompting by using domain-guided intermediate representations across code, specs, and proofs.
citing papers explorer
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AxDafny: Agentic Verified Code Generation in Dafny
AxDafny achieves 92.7% verification success on DafnyBench (6.5 points above prior proof-hint baselines) via verifier-guided repair and introduces the LCB-Pro-Dafny benchmark of 250 problems.
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Formal-Method-Guided Vibe Coding: Closing the Verification Loop on AI-Generated Safety-Critical Software Through Model-Driven Engineering
Forge pipeline combines LLM code generation with MDE transformations to produce verifiable artifacts in Dafny, CSP, and Isabelle, iterating on failures to generate standards-relevant evidence for Java code.
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Natural Language based Specification and Verification
LLMs can generate natural language specs and perform compositional verification to help prevent vulnerable code from being produced by AI models.
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BRIDGE: Building Representations In Domain Guided Program Synthesis
BRIDGE improves Lean executable correctness up to 1.5x and sample efficiency roughly 2x over direct prompting by using domain-guided intermediate representations across code, specs, and proofs.