autoresearch-create

Automate hyperparameter optimization by defining a goal, metric, and scope to initialize an autonomous experiment loop. The loop runs rapid experiments, logs results, and automatically reverts changes on failure, keeping only successful modifications for continual refinement.

An autonomous optimization loop is an iterative experimentation process that automatically configures baselines, logs metrics, and refines code, data, or models. You need it when running rapid, reproducible experiments that require continual refinement and automatic reversion of failed changes.

Set up a reproducible workflow by generating dedicated files including autoresearch.md, autoresearch.sh, and autoresearch.config.json. These files control iterations, log results automatically, and apply optional quality checks to ensure experimental consistency across code, data, and model changes.

Yes, you can configure a custom goal, metric, and scope to define the boundaries of your autonomous experiment loop. This configuration initializes the session, runs the baseline, and starts iterating immediately within your specified parameters.

Yes, the autonomous experiment loop supports automatic reversion of changes on failure. When an experiment iteration fails to improve the defined metric, the loop automatically reverts the modifications and continues iterating with new variations.

Autonomous optimization sessions require clearly defined goals, metrics, and scope to function effectively, and rely on dedicated configuration files for reproducibility. Without optional quality checks enabled, failed iterations may not be properly guarded against introducing unintended side effects.