iterative-experiment

An autonomous optimization loop automates measurable improvement by generating hypotheses, applying scoped changes, committing them, measuring the benchmark metric, and reverting regressions. It keeps only modifications that yield a measurable improvement.

The best way is to use a structured autonomous loop that establishes a baseline measurement, proposes isolated changes within a defined scope, and commits only improving iterations. This ensures modifications remain controlled and reproducible.

Yes, you need a git-clean starting state before starting automated benchmarking or schema changes. The optimization loop requires this to safely commit changes and automatically revert on failure without risking unintended edits to your repository.

Revert-on-failure works by measuring the benchmark metric after each isolated code modification; if the iteration does not improve the baseline, the loop automatically reverts the commit. This guarantees that only performance-enhancing changes are kept.

Yes, you can log experiment iterations to a TSV file. The loop persists a deterministic TSV experiment log of every iteration, including commits and deltas, under a configured results path to ensure reproducible experiment logging.

Limitations of hypothesis-driven change loops include requiring a single numeric metric for evaluation and a bounded iteration budget. Modifications must also be isolated to a defined scope to prevent unintended edits, meaning it cannot handle unmeasurable or unbounded tasks.