| name | tournament-autoresearch |
| description | Use when the user wants an autonomous ML research loop that pressure-tests competing ideas before spending compute — several research subagents each propose one architecture change, a self-calibrating Judge critiques them against a rubric, the proposers refine, and the Judge picks the single change to run. The Judge learns to pick better over time by scoring its own predictions against realized metric deltas, recording predicted-vs-realized in a calibration ledger and refining its working rubric. The result is an experiment ledger where each iteration's change won a de-biased tournament. Not for running a single pre-decided experiment, and not for analysis-only exploration — for one hypothesis proposed and run per iteration without competition, use the sibling ml-autoresearch loop.
|
| compatibility | Requires Python 3.9+ |
| metadata | {"version":"0.1.0"} |
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.
The cast and files (all in this folder):
roles/Judge.md — your behavior: critique, rank, decide, self-calibrate.roles/ResearchAgent.md — the proposer role, spawned <n> times each round.rubrics/rubric.md — the scoring criteria (shipped defaults; copied to a working copy at setup).schemas/idea.schema.json — what a proposer returns (one proposed change).schemas/verdict.schema.json — what the Judge records per idea (scores, rank, decision).
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) infer a likely value for each binding and present it as the recommended option; on other
hosts ask each as a quoted plain-text prompt. Then write loop.run.yaml (format:
examples/run.example.yaml) and confirm the values before creating any other files.
Record <host> (claude-code or other) once — it also decides spawn-or-degrade: on Claude Code
spawn real Agent subagents for the proposers, all in one turn; otherwise adopt the ResearchAgent
role inline, one proposal at a time.
| binding | meaning | default | how to infer |
|---|
<metric> | scalar metric to optimize | — | infer from code/README; ask direction |
<metric_direction> | minimize or maximize | — | infer from the metric's meaning |
<run_cmd> / <entrypoint> | command that runs one experiment end to end | — | pyproject.toml/.venv/README |
<editable_files> | model/config/training files the loop may edit; never the eval harness | — | scan for the model + training script |
<sandbox_root> | where snapshots + ledgers live | ./sandbox | — |
<iter_strategy> | branches or snapshots | snapshots | git present → offer branches |
<gate> / <budget> | time (minutes) or epochs, plus the cap | epochs / — | infer epoch arg from the script |
<n> | proposers competing each round | 3 | recommend 3 — competition without a crowd |
<refine_rounds> | propose→critique→refine rounds before the Judge decides | 1 | — |
If <iter_strategy> is branches, create git checkout -b autoresearch/<run_tag> (must not exist).
If <gate> is time, write <sandbox_root>/run_with_timeout.sh
(timeout $(( <budget> * 60 )) <entrypoint> "$@") and hard-kill at 2 × <budget> min; if epochs,
cap the epoch count in an editable file.
Initialize the sandbox (after confirmation):
<sandbox_root>/
├── loop.run.yaml ← resolved bindings (written now)
├── results.tsv ← experiment ledger, header only
├── calibration.tsv ← Judge predicted-vs-realized ledger, header only
├── judge_lessons.md ← append-only Judge lessons (header only)
├── rubric.active.md ← copy of rubrics/rubric.md; the Judge self-refines THIS, never the shipped one
└── iter1/ ← created at loop start
Copy rubrics/rubric.md → <sandbox_root>/rubric.active.md. Write the headers (see Ledger).
The loop
<run_log> = the file capturing training output for an iteration (default
<sandbox_root>/iter<N>/run.log). Everything in <editable_files> is fair game; code must run and
finish within <budget>. Simplicity criterion: equal metric but simpler code is a keep. The
tournament yields exactly one change per iteration. Iteration 1 is the unmodified baseline —
skip the tournament; just run + analyse to seed the first analysis summary.
Copy this checklist and tick items off, looping until interrupted:
In detail, each iteration:
- State. branches:
git log --oneline -5. snapshots: confirm iter<N>/ is new.
- Tournament (iter 2+). As the Judge (
roles/Judge.md): spawn <n> ResearchAgents
(spawn-or-degrade by <host>) with roles/ResearchAgent.md, the latest analysis summary, and
schemas/idea.schema.json. Critique & score each against rubric.active.md — gate (reject ideas
with no testable prediction, unscored) → pointwise 0–5 per axis as the learning signal →
de-biased pairwise to rank (compare each pair in both orders, keep only consistent verdicts).
Refine <refine_rounds>×, re-score, then select the single rank == 1 change (no merging).
Write ideas to iter<N>/ideas/, one verdict per idea to iter<N>/verdicts/, and log the Judge's
predicted outcome for the winner to calibration.tsv.
- Snapshot/commit, then apply the winning change. snapshots: copy
<editable_files> →
iter<N>/code_snapshot/, copy loop.run.yaml → iter<N>/, apply the change. branches: apply,
git commit -am "<idea_id>: <short description>".
- Analysis plan →
iter<N>/analysis/plan.md: a deliverables table including the winner's
prediction and any useful analysis steps from the losing ideas. ≥1 row must cover a
not-yet-measured dimension.
- Run (redirect to
<run_log>, never tee) → read the metric (grep '^<metric>:' <run_log>;
on empty, tail -n 50, one trivial fix, else log crash).
- Analyse — mandatory, real artifacts. Execute every
plan.md row → files in iter<N>/results/;
verify none missing; interpret; check the winner's prediction; write a 3–8 bullet analysis
summary ending in the empirical anchor for the next round.
- Log. Append the
results.tsv row (0.000000 on crash). Append the realized delta + to
against the prediction.
Never stop. Once running, do not pause to ask "should I continue?" — the loop runs until manually
interrupted. If ideas run dry: push proposal diversity, mine results.tsv/calibration.tsv for
under-explored directions, go deeper on analysis.
Ledger
Three append-only files under <sandbox_root>, all tab-separated, never commas in free text.
results.tsv — the experiment ledger (same format as ml-autoresearch). Header:
iter <metric> status analysis_summary description
status ∈ {keep, discard, crash}. Example:
iter val_acc status analysis_summary description
1 0.6320 keep baseline; grad norms even, no pathologies baseline
2 0.6890 keep layer-2 activations near-saturated; BN helped iter2-a1: add BatchNorm after conv2
calibration.tsv — the Judge's track record (predicted vs realized). Header:
iter idea_id grounding impact feasibility pred_direction pred_magnitude confidence realized_delta hit
Example:
iter idea_id grounding impact feasibility pred_direction pred_magnitude confidence realized_delta hit
2 iter2-a1 5 4 4 improve +2% high +0.057 1
judge_lessons.md — append-only prose, 1–3 bullets per iteration: which axis tracked gains, what
kind of idea was over/under-rated, and the reason for each rubric.active.md refinement. Example:
## iter 2
- grounding tracked the gain (BN tied to the dead-unit finding hit +0.057, as predicted).
- bumped grounding weight 0.40 → 0.45; tightened the impact anchor (impact=5 picks over-promised).
Report the best iteration (highest keep metric), not necessarily the last, plus the running
selection hit-rate. Leave results.tsv, calibration.tsv, judge_lessons.md, rubric.active.md,
and iter*/ untracked.
Constraints
- Only edit files in
<editable_files> — confirm before every edit, because everything else
(especially the eval harness) is read-only ground truth defining <metric>.
- Exactly one change per iteration (the tournament winner) — no merging ideas — so each metric
delta is attributable to one change.
- An idea with no testable
prediction is rejected before scoring; the rank is decided by
de-biased pairwise comparison, never by the pointwise scores (which only feed calibration).
- The Judge edits only
rubric.active.md (the working copy), never the shipped rubrics/rubric.md.
- Always redirect training output to
<run_log>; never tee (it floods your context).
- Do not install packages or add dependencies the project lacks; helper code stays stdlib-only.
- Do not modify the evaluation harness, and do not pause the loop to ask for direction.
- The sandbox must be self-contained — no
../ escapes.
