research-progress-tracker

To track experiment progress and maintain reproducible research workflows, you can enforce end-to-end experiment tracking through systematic updates to PROGRESS.md, PIPELINE.md, and EXPERIMENTS.md alongside strict git commit discipline.

You ensure configuration consistency before model training and evaluation by running automated consistency checks that validate matching inference parameters, such as mask-logit and all-logit settings, against your data generation and training configurations.

The best way to log failed experiments for academic model training is to systematically register all pipeline changes and experiment outcomes, including failures, into EXPERIMENTS.md and EXPERIMENTS.csv for continuous iteration and historical traceability.

Yes, this research workflow tracker validates your conda environment before running scripts by checking whether your current environment aligns with the project's recommended conda configuration during the startup and evaluation gating phases.

To start managing your research code iteration, you trigger the workflow by typing the exact phrase “科研启动” and then describe your specific code or experiment task, which activates the progress logging and consistency gating mechanisms.