exploratory-autoresearch

Exploratory Autoresearch Loop

This loop runs hot. Like the standard ml-autoresearch, every experiment is followed by a diagnostic analysis pass. Unlike it, the type of change at each iteration is set by a temperature scheduler, not the agent's intuition: it forces wide, diverse swings early (full rewrites, fundamentally different architectures and training regimes), then drops into an adaptive phase that chooses between swing (a fresh wild approach), merge (combine two registered approaches), or exploit (a focused tweak of the best). A stagnation guard bans exploit once it has run <stagnation_limit> times in a row, forcing a pivot back to swing or merge so the loop never gets stuck hill-climbing. The feedback signal is <metric> read from the run log; an approaches.md registry and a move_type per iteration are what make the scheduler work.

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

When to use

Use for an open-ended ML campaign where you want forced breadth before refinement — the scheduler guarantees you sample several distinct families before converging, and the stagnation guard prevents endless small steps. Default to <swing_budget> = 3 and <stagnation_limit> = 3; raise <swing_budget> for wider initial exploration. Not for the standard analysis-first ml-autoresearch (use that when you want the analysis alone to drive each change, with no forced-swing scheduler), not for a single training run or a fixed sweep, and not for tasks with no measurable scalar metric.

Setup

Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it, confirm the values in one line, and skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is available — record <host> = claude-code) infer a likely value for each binding from the project and present it as the recommended option; on other hosts (<host> = other) ask each as a quoted plain-text prompt. Then write loop.run.yaml (format: examples/run.example.yaml) and confirm every value with the user before creating any other files. For branches strategy, create git checkout -b autoresearch/<run_tag> (tag from today's date; branch must not exist). For time gating, write <sandbox_root>/run_with_timeout.sh (timeout $(( <budget> * 60 )) <entrypoint> "$@") and use it as the run command, hard-killing at 2 × <budget> min; for epochs, patch the epoch cap in an <editable_files> file.