Is your feature request related to a problem? Please describe.
ML Intern can research, train, evaluate, and ship ML projects autonomously. However, reproducing successful runs can be difficult because key decisions are spread across agent traces, generated code, datasets, model choices, and training configurations.
When comparing multiple ML Intern runs, it is not always obvious which decisions contributed to better outcomes.
Describe the solution you'd like
Generate a reproducibility report at the end of each completed project containing:
- Dataset versions used
- Model and checkpoint selections
- Hyperparameters
- Generated training scripts
- Evaluation metrics
- Compute usage
- Key agent decisions and reasoning checkpoints
The report could be exported as Markdown and attached to the final project artifacts.
Describe alternatives you've considered
Users can manually inspect logs, session traces, and generated files, but this becomes difficult for longer autonomous workflows.
Additional context
This would make ML Intern outputs easier to reproduce, compare, and share while improving confidence in autonomous ML experimentation.
Is your feature request related to a problem? Please describe.
ML Intern can research, train, evaluate, and ship ML projects autonomously. However, reproducing successful runs can be difficult because key decisions are spread across agent traces, generated code, datasets, model choices, and training configurations.
When comparing multiple ML Intern runs, it is not always obvious which decisions contributed to better outcomes.
Describe the solution you'd like
Generate a reproducibility report at the end of each completed project containing:
The report could be exported as Markdown and attached to the final project artifacts.
Describe alternatives you've considered
Users can manually inspect logs, session traces, and generated files, but this becomes difficult for longer autonomous workflows.
Additional context
This would make ML Intern outputs easier to reproduce, compare, and share while improving confidence in autonomous ML experimentation.