Tournament Autoresearch Loop

An ML autoresearch loop whose single "form a hypothesis" step is replaced by an idea tournament. The artifact is an experiment ledger; the feedback signal is the realized <metric> delta of the change that won the tournament. Each iteration <n> ResearchAgents propose competing architecture changes, a Judge critiques and ranks them, the proposers refine, and the Judge selects one change to run. The Judge is the orchestrator and self-calibrates: it scores its predictions against realized results, so it learns which kinds of ideas actually pay off. The experiment mechanics (snapshot → run → mandatory analysis → keep/revert) match the sibling ml-autoresearch loop.

When to use

Use this for open-ended ML experimentation where competing ideas should be vetted before compute is spent and the picker should improve over time. You are the Judge: adopt roles/Judge.md and spawn the proposers with roles/ResearchAgent.md. Default to <n> competing proposers with one refine round; widen <n> or add rounds when ideas are converging too fast. Not for running a single pre-decided experiment, and not for analysis-only exploration over a dataset — for one uncompeted hypothesis per iteration use the sibling ml-autoresearch loop.