ablation-planner

To design an ablation study, you need a structured plan that isolates component contributions, defines specific test questions, and estimates compute requirements to validate project results. The planner generates this outline from reviewer feedback.

The best way to plan experiments for validating ML research results is to generate a structured ablation plan that specifies components, expected outcomes, run order, and compute estimates. This ensures rigorous validation and isolates component contributions effectively.

You can estimate compute requirements for an ablation study by prioritizing config-only changes and aligning the planned tests with project constraints. The generated plan includes specific compute estimates and a defined run order for feasibility.

Yes, you can automate ablation review by using an auto-review loop that identifies missing ablations based on pass/fail results. This process specifies necessary components, tests, and expected outcomes to ensure comprehensive validation.

Yes, this ablation planner is explicitly applicable to projects with pass/fail results where an explicit ablation request is made. It produces an actionable plan for review, experimentation, and result logging under these conditions.

A structured ablation plan requires an explicit ablation request and available project results to function properly. Without explicit reviewer feedback or pass/fail results, the auto-review loop cannot identify missing ablations or generate actionable testing steps.