limen

The same-configuration noise floor of an evaluation, and whether a published ranking clears it.

limen turns a directory of repeated identical evaluation runs into the three numbers that decide whether a comparison is real: the fraction of items whose pass/fail verdict is not constant across identical repeats, the noise floor a claimed improvement has to clear at the k you actually ran, and how often a leaderboard computed from a single run disagrees in sign with the leaderboard computed from all of them. These are emitted as per-pair SIGN-STABLE / SIGN-UNSTABLE rulings with every denominator shown, plus a CI gate that fails a benchmark report claiming a gain smaller than that report's own measured draw noise.

What it does not do, and this is the whole boundary of the instrument: it makes no statement about which model is better. A sign flip means "this comparison is not supported by its own measurement", never "the other one wins".

The quantities are not new, and the docs say so up front: the constant-verdict fraction upper-bounds TARa@N (Atil et al.), the rank-flip rate is IR's swap rate, psychometrics calls the per-item question decision consistency, quality engineering has had it for seventy years as attribute agreement analysis, and genomics' IDR is the exclude-then-re-rank step. The literature exists. An installable instrument did not. That is the whole claim.

Install

pip install limen-eval

Zero runtime dependencies, Python >= 3.12. The distribution is named limen-eval (the bare name was taken); the import and the CLI are limen.

90 seconds

# a synthetic archive whose right answers are chosen (the instrument's oracle)
limen synth --out demo --models 3 --items 500 --draws 8 --flaky-fraction 0.05 --gap 0.02

# repeated-run logs -> a versioned ruling document
limen report demo/archive.verdicts.csv.gz --out demo-report

# fail CI when a claim does not clear its own noise
limen gate demo-report/report.json --require-sign-stable --min-effect-vs-noise 1.0

limen report reads:

• a generic long-format CSV (model, task, item_id, draw_id, verdict, plus optional score, collected_at, model_version, raw_sha256),
• lm-evaluation-harness --log_samples output trees (run the harness at least twice, or with repeats: limen needs k >= 2 draws per item),
• inspect_ai .eval logs at the per-epoch layer (each epoch is one draw; no inspect_ai dependency needed).

The ruling document

One deterministic JSON document per archive: per-(model, task) flakiness with always-pass / always-fail / mixed counts, the spread of the k single-draw scores with a minimum detectable difference at the observed k (after Kalibera & Jones), per-pair sign-stability rulings with drawn ties counted separately, a stable-items-only re-ranking that ships only together with its selection-bias mitigations, a drift guard whose missing-field state is UNAVAILABLE and never PASS, and a grader-defect count (verdict flips on byte-identical output are the grader's flakiness, not the model's). Regeneration from the same input is byte-identical. Rulings are versioned and immutable.

GitHub Action

- uses: KurathSec/limen@main
  with:
    report: limen-report/report.json
    require-sign-stable: "true"
    min-effect-vs-noise: "1.0"

Status

Alpha. The measurement definitions are frozen behind a versioned rulings spec (limen spec list); changing any recorded meaning is a spec version bump, not a patch. See docs/honesty.md for the full list of things this instrument deliberately does not claim.

License

MIT.