Author-in-the-loop Agentic Reproducibility Auditing for a Machine Learning Conference

Explain Like I'm Normal

Researchers deployed an AI agent to automatically audit the reproducibility of code submitted to machine learning conferences. The system follows author instructions on GitHub, identifies missing dependencies or bugs, and loops in the humans for clarifications when the code fails to run. This moves peer review from a 'trust me bro' model to a verifiable, agent-verified pipeline.