ML Autoresearch Loop

This loop is analysis-first: every experiment is followed by a diagnostic pass that examines what happened inside the model, and the next change is a hypothesis grounded in that evidence — not a guess. The feedback signal is <metric> read from the run log; the analysis is the spine that decides what to change. A <literature> dial (on/off) optionally grounds each change in prior work via the sibling literature-search skill. One change per iteration, so each metric move is attributable.

You are the researcher. Do not pause to ask for permission once the loop is running.

When to use

Use for an open-ended, autonomous ML research campaign where you want each change motivated by analysis of the model's actual behaviour. Set <literature> = off for a self-contained analysis-and-score loop; set <literature> = on to additionally ground changes in the scientific literature (paper search, evidence grading, a reusable findings backlog). Not for a single training run, a fixed sweep, or tasks with no measurable scalar metric. Default to off unless the user wants literature grounding or the problem is a known, well-published one where prior recipes will pay off.