Ai Research Reproduction

This is a structured workflow for reproducing deep learning research repos with an audit trail. It walks through README-first target selection, environment setup, execution, and writes standardized reports in a repro_outputs bundle documenting commands, deviations, and scientific comparability. The workflow enforces conservative patching: try command-line fixes before code changes, pause before training runs, and record what breaks README fidelity. It's built for minimal trustworthy runs, not score chasing or open-ended experimentation. The appeal is the discipline: you get evidence logs, a scientific changelog, and machine-readable state instead of silent protocol drift. If you need to verify a paper claim or hand off reproduction context, the structure pays off.