MedEval-1 · the held-out track

running

Every public benchmark rots: it is published, it becomes the reference, it is scraped into the next model's training data, and from that moment a high score measures memorisation instead of ability. There are only two stable answers — keep the test set secret, or continuously replace it. This track does both, and below it does it in front of you.

Public-data demonstration of the private protocol

The cycle, executing

Mint → seal → score → rotate → audit. Every panel below is a replay of one real run; nothing is illustrative.

Append-only ledger

Two sealed generations

The shape is published. The contents are not. The hash is written into the ledger before anything is scored, which is what makes “this set existed in final form first” checkable rather than assertable.

The contamination catch

Run the same model on the sealed private set and on the public control arm. For an honest model the two agree to within sampling error. The comparison only exists because one harness runs both tracks — which is why the free public leaderboard runs forever: it is the control arm.

Public-data demonstration Public corpus stand-in (BloodMNIST). The leak below was created deliberately, for the demonstration. No private, restricted or patient-level data.

Public arm vs sealed generation 1

public control arm sealed private set flagged
Chance-corrected balanced accuracy ×100. Each rule is one submission; its length is the gap the detector tests.

The gap statistic

no gap beyond sampling error flagged, |z| ≥ 3
z = (public − private) ÷ bootstrapped standard error. The ±3 threshold was written into the ledger before any set was minted.

Rotation neutralises it

Generation 1 expired on its published date, was retired, and was republished in full as a dated, known-contaminated public benchmark. Generation 2 is fresh accrual the leaked model has never seen.

Public-data demonstration Public corpus stand-in (BloodMNIST). The leak below was created deliberately, for the demonstration. No private, restricted or patient-level data.

The leaked model's advantage over the same model trained honestly

Points of chance-corrected balanced accuracy, on the ranked partition of each generation.

Anchor calibration, so the scale survives the rotation

Rotation creates a measurement problem it also has to solve: a fresh generation is not the same difficulty as the one it replaces. A -case anchor sits inside both generations — the same physical cases — so the difference is measured, not assumed. The anchor never enters a ranked score, and a flagged model is excluded from the calibration pool so contamination cannot move the scale.

Held-out probes: cross-generation discrepancy

raw after anchor calibration
Three operator reference models, the identical fitted object scored on both generations, held out of the calibration pool. Their ability is fixed by construction, so any score change is generation difficulty.

Audit: the number re-derived without the data

The auditor receives the seal manifest, the ledger, and a de-identified bundle — per case, a salted identifier, its published hash, the true label and the predicted probabilities. No image. No feature. No patient attribute. From that alone the published score is re-derived and the ordering of seal-before-score is proved. This is the artefact a regulator eventually asks for.


What this run really is

    What it is not