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A system that leverages LLMs to automatically generate IJON-style annotations is designed and it is observed that LLMs can generate annotations that achieve comparable results to human generated ones, opening the door for future research to perform further studies on the impact of annotations at scale.
LLM-based vulnerability repair can be significantly improved by focusing on root cause analysis, leading to more robust and less superficial patches than current methods.
