Dueling Autoresearch Loop

Two lanes work the same objective in parallel and race the same metric — by default a classical/algorithmic lane against an ML/learned lane (the lanes are user-named). Each lane runs its own analysis-first iteration via roles/TrackAgent.md, confined to its lane. Every round both lanes post to a shared duel_log.md scoreboard and may borrow ideas across the lane boundary — but each stays in its lane. The feedback signal is the shared <metric> on a shared eval: if the classical lane wins, that is a real result. Lanes support mixed code locations — a codebase lane edits existing repo files, a sandbox lane authors its own code — and an eval-parity gate keeps the scores comparable.

You are the orchestrator: each round you advance both lanes, update the scoreboard, and keep both honest. Do not pause for permission once the loop is running.

When to use