ar-autoresearch

Automating machine learning metric optimization requires an autonomous research loop that iteratively mutates a single editable file, runs experiments, and commits changes via git to continuously optimize the target metric.

An autonomous research loop is a continuous process that mutates an editable file, executes experiments, and logs numeric metric results to drive iterative optimization in machine learning workflows without human check-ins.

To set up an iterative experiment loop, initialize a new git branch for the autoresearch process, ensure Python with pandas and numpy are installed, then execute the autonomous loop skill to start mutating files and logging results.

Yes, autonomous metric tuning requires Python along with pandas and numpy libraries to handle file manipulation, numeric metric evaluation, and experiment logging throughout the iterative optimization process.

No, the autonomous research loop is specifically designed to iteratively mutate a single editable file to optimize a numeric metric, making it best suited for localized parameter tuning rather than multi-file orchestration.

The loop commits changes during metric optimization to maintain a versioned history of file mutations and experiment results via git, ensuring each iterative step is tracked and recoverable throughout the tuning process.