autoresearch

Automated performance tuning uses an autonomous experiment loop to test optimization hypotheses, measure target metrics, and retain only valid improvements. It systematically reverts regressions without requiring manual intervention or repeated session setups.

Reducing test runtime or bundle size is achieved by setting a measurable target metric and a termination condition. The system automatically tests changes, evaluates results against the baseline, and finalizes improvements into mergeable git branches.

Confidence scoring applies Median Absolute Deviation (MAD) to evaluate experiment results. This statistical method distinguishes genuine metric improvements from random noise, ensuring only statistically valid optimizations are kept.

Resuming optimization experiments across sessions is fully supported through structured JSONL state tracking. It maintains detailed experiment logs and a living research document, allowing any new session to pick up exactly where prior work left off.

The autonomous optimization loop uses git branch isolation to test hypotheses safely. Valid improvements are automatically finalized into independent, mergeable branches, keeping the main codebase clean and stable.

Tracking ML training loss optimization is best handled by an autonomous loop that measures results against the primary metric. It logs all experiment states in structured JSONL files and automatically reverts any failed changes.