lamm-mit Research Experiment Skill Summary
This skill defines a workflow for conducting comprehensive research experiments in machine learning projects. Here's the concise breakdown:
Core Purpose
Execute full training runs, ablation studies, and iterative supplementary experiments with systematic analysis at each stage.
Key Workflow Steps
Full Training: Run the model with production epoch counts from the research plan, recording all metrics and loss values.
Result Analysis: Evaluate convergence, overfitting patterns, and training stability from the output logs.
Ablation Studies: Conduct 2-3 component removal experiments (2 epochs each) to measure individual contribution.
Iterative Analysis & Supplementary Experiments (2 rounds):
- Analyze current results and propose targeted follow-up experiments
- Execute sensitivity analysis, visualizations, robustness tests, or baseline comparisons
- Re-analyze findings and repeat
Final Report: Compile comprehensive results across all experiment categories into a structured markdown document.
Critical Constraints
- Modify only epoch counts and experiment parameters, never core algorithm logic
- All reported numbers must come from actual execution output
- Two rounds of supplementary experimentation are mandatory
- Requires validation that prior research review verdict shows "PASS"
Output Deliverables
- Complete experiment report with full results, ablations, and supplementary findings
- Round-by-round analysis documents tracking the iterative discovery process