LeetProof achieves higher rates of fully certified program synthesis from natural language by using a multi-modal verifier in Lean to validate specifications via randomized testing and delegate proofs to AI tools, outperforming single-mode baselines on benchmarks while uncovering defects in prior参考.
A benchmark for vericoding: formally verified program synthesis
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Viverra generates C code from text descriptions together with assertions that are verified by model checkers, and a user study with over 400 participants shows the verified assertions improve code comprehension.
VeriContest supplies 946 problems with specs, code, proofs, and tests to benchmark verifiable code generation in Rust/Verus, showing models reach 92% on code but only 5% end-to-end on full verifiable synthesis.
Training Qwen3-8B on symbolic execution traces from Soteria improves violation detection in C programs by over 17 points, transfers across five property types, and shows superadditive gains with chain-of-thought.
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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Certified Program Synthesis with a Multi-Modal Verifier
LeetProof achieves higher rates of fully certified program synthesis from natural language by using a multi-modal verifier in Lean to validate specifications via randomized testing and delegate proofs to AI tools, outperforming single-mode baselines on benchmarks while uncovering defects in prior参考.
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Viverra: Text-to-Code with Guarantees
Viverra generates C code from text descriptions together with assertions that are verified by model checkers, and a user study with over 400 participants shows the verified assertions improve code comprehension.
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VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation
VeriContest supplies 946 problems with specs, code, proofs, and tests to benchmark verifiable code generation in Rust/Verus, showing models reach 92% on code but only 5% end-to-end on full verifiable synthesis.
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Teaching LLMs Program Semantics via Symbolic Execution Traces
Training Qwen3-8B on symbolic execution traces from Soteria improves violation detection in C programs by over 17 points, transfers across five property types, and shows superadditive gains with chain-of-thought.
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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.