VLP adds an NL documentation layer with trace-linked mismatch detection and derived formal checks to make human validation of LLM code feasible, lifting pass@1 from 28.7-73.2% to 65.4-93.5%.
Python Symbolic Execution with LLM-powered Code Generation, September 2024
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ConcoLixir uses a reactive LLM oracle to improve line coverage in Python concolic testing by 8.6 to 17 percentage points on synthetic, real-world, and library targets.
A framework combining legal ontology, rule extraction, and solver reasoning verifies whether AI explanations for CalFresh eligibility align with statutory constraints.
citing papers explorer
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Guiding Human Validation of LLM-Generated Code via Verifiable Literate Programming
VLP adds an NL documentation layer with trace-linked mismatch detection and derived formal checks to make human validation of LLM code feasible, lifting pass@1 from 28.7-73.2% to 65.4-93.5%.
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ConcoLixir: Reactive LLM Discovery Oracles for Python Concolic Testing
ConcoLixir uses a reactive LLM oracle to improve line coverage in Python concolic testing by 8.6 to 17 percentage points on synthetic, real-world, and library targets.