Synthetic sample / No candidate data

See what the reviewer receives.

Choose a role. Trace a finding to the work, inspect what the checks establish, and find a useful follow-up question. All four briefs use fictional examples; they contain no candidate data.

SeeTheWork / Evidence brief

ML research

Synthetic sample

The experiment is traceable. The claim needs a wider check.

The sample records seeds and settings, compares against a matched baseline, and limits its conclusion to the tested setup. Evidence for broad adoption remains incomplete. [04]

Builds a reproducible experiment

Supported in this example

[01][02]

Makes a controlled comparison

Supported in this example

[03]

Calibrates an adoption recommendation

Follow-up needed

[04]

All excerpts below are illustrative, including filenames and check results. They explain the review method and contain no real candidate data.

  1. [01]

    Experiment manifest

    Illustrative

    Record the conditions.

    experiment.yaml

    seeds: [11, 23, 37]
    training_steps: 1200
    data_split: fixed_split_v1
    methods: [baseline, proposed]
    record: [configuration, runtime, metric, failures]
    What it supports
    The manifest states the settings needed to interpret and repeat the comparison.
    What remains open
    An executable environment and dependency versions are also needed for reproduction.
    Back to competency evidence
  2. [02]

    Run ledger

    Illustrative

    Keep unsuccessful attempts visible.

    run_ledger.csv · illustrative excerpt

    seed,method,status
    11,baseline,complete
    11,proposed,complete
    23,baseline,complete
    23,proposed,failed_and_recorded
    What it supports
    The ledger makes a failed attempt visible instead of silently removing it from the record.
    What remains open
    The failure needs diagnosis; this excerpt alone does not establish its cause.
    Back to competency evidence
  3. [03]

    Comparison plan

    Illustrative

    Compare matched experiments.

    analysis.md

    Pair runs by seed and data split.
    Report each result and the spread.
    Track compute consumed by both methods.
    Do not replace failed runs without disclosure.
    What it supports
    The plan accounts for experimental variation and resource cost when comparing methods.
    What remains open
    Few runs and one dataset provide limited support for generalization.
    Back to competency evidence
  4. [04]

    Research verdict

    Illustrative

    Tie the recommendation to the evidence.

    recommendation.md

    “Continue investigation; do not adopt yet.
    Resolve the failed run and test a second setting.
    The current result supports only this setup.”
    What it supports
    The recommendation names the next work and limits the claim to available evidence.
    What remains open
    The reviewer should ask how the next experiment was prioritized within the budget.
    Back to competency evidence

Which next experiment would most change your adoption recommendation within the remaining budget?

Evidence for human review
Not an automated verdict

What would evidence like this help your team understand about its next hire?