SkillGuard extracts executable environment contracts from LLM skill documents to detect only relevant drifts, reporting zero false positives on 599 cases, 100% precision in known-drift tests, and raising one-round repair success from 10% to 78%.
Intent formalization: A grand challenge for reliable coding in the age of ai agents
3 Pith papers cite this work. Polarity classification is still indexing.
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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%.
Agentic Claude reaches 98.8% valid specs, 87.5% implementation certification, and 98.1% end-to-end success on CLEVER, revealing a mismatch between benchmark difficulty and current prover performance.
citing papers explorer
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Skill Drift Is Contract Violation: Proactive Maintenance for LLM Agent Skill Libraries
SkillGuard extracts executable environment contracts from LLM skill documents to detect only relevant drifts, reporting zero false positives on 599 cases, 100% precision in known-drift tests, and raising one-round repair success from 10% to 78%.
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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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Agentic Proving for Program Verification
Agentic Claude reaches 98.8% valid specs, 87.5% implementation certification, and 98.1% end-to-end success on CLEVER, revealing a mismatch between benchmark difficulty and current prover performance.