AgenticRepair uses three specialized LLM subagents to assemble code-structure, runtime-execution, and commit-history context before a dedicated repair agent synthesizes a vulnerability patch. According to the supplied abstract, evaluation on 300 real-world SEC-Bench instances with sanitizer-based verification yields a 73% success rate, reported as 29% above the strongest baseline. Ablations reportedly find the three context types complementary and identify both multi-agent scaffolding and base-model capacity as important. However, the supplied arXiv identifier and July 31, 2026 publication date are future-dated, so the paper and its claims cannot yet be independently verified.
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