SOURCE-LINKED INTELLIGENCE
MAGS: Multi-agent Auto-formalization Guarantees Safety for Agentic Outputs
LLM coding agents now generate complex programs at a scale that makes thorough human review increasingly difficult, raising the risk of safety and security failures. Common approaches, including fuzz testing, static analysis, and LLM-as-a-Verifier, can detect many failures but struggle to cover all possible edge cases. Formal verification addresses this by providing machine-checkable guarantees over specified properties, but traditionally demands substantial manual specification and proof engineering. We introduce a unified multi-agent framework, MAGS, that generates executable programs with f
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T20:15:36.000Z
- arXiv · Artificial Intelligence · 2026-09-16T20:15:36.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.