LORIS detects local reasoning errors in LLM-generated proofs for loop invariants by translating natural-language steps to first-order logic implications and using invalid implications to refine the invariants, achieving 93.1% success on 460 C programs.
arXiv preprint arXiv:1909.11542 (2019)
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SpecSyn generates formal specifications with over 90% precision and 75% recall, successfully verifying 1071 out of 1365 target properties on open-source programs.
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Guiding LLM-based Loop Invariant Synthesis via Feedback on Local Reasoning Errors
LORIS detects local reasoning errors in LLM-generated proofs for loop invariants by translating natural-language steps to first-order logic implications and using invalid implications to refine the invariants, achieving 93.1% success on 460 C programs.
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SpecSyn: LLM-based Synthesis and Refinement of Formal Specifications for Real-world Program Verification
SpecSyn generates formal specifications with over 90% precision and 75% recall, successfully verifying 1071 out of 1365 target properties on open-source programs.