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A benchmark score is an experiment record, not a lone leaderboard number. A result is reproducible when another team can identify the exact task, data, system, environment, randomization, analysis, and artifacts that produced it, then rerun the procedure and inspect the same evidence. The report must also show uncertainty and validity limits: a perfectly rerunnable pipeline can still measure the wrong construct or a biased sample.
Use the checklist below to turn an AI evaluation into an auditable package. It follows reporting expectations reflected in AAAI, NeurIPS, and NAACL guidance, the World Bank’s guidance for AI-assisted work, and the HumanEval methodology page.
What reproducible benchmark reporting actually means
Reproducibility has several layers, and they should not be conflated:
- Procedural reproducibility: a reader can start from a clean checkout, install the declared environment, run the documented commands, and obtain the published output files.
- Result reproducibility: repeated runs under the declared protocol produce results whose variation is reported rather than hidden.
- Measurement validity: the task, population, metric, and aggregation answer the decision question the study claims to address.
- External validity: the benchmark’s sample and conditions support the intended real-world conclusion.
Only the first two are guaranteed by a runnable repository. Contamination, sampling bias, a weak construct, metric gaming, an unreliable judge, or missing subgroups can invalidate an interpretation even when every command works.
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The one-page checklist
| Check | Record in the report | Evidence a reader should receive |
|---|---|---|
| 1. Measurement target | Task, population, hypotheses, inclusion and exclusion rules, benchmark name and version, metric direction, and motivation for each metric | Formal metric definitions and a statement of the decision each metric informs |
| 2. Pinned data | Dataset revision or content hash, license, provenance, preprocessing, filtering, deduplication, splits, and contamination or leakage checks | Permitted data, an access procedure, or a preserved checksum and snapshot description |
| 3. System under test | Model family, exact checkpoint or API date, tokenizer, prompt or template, decoding settings, tools, stopping rules, and final hyperparameters | A configuration file or manifest that fully identifies the evaluated system |
| 4. Execution environment | Operating system, CPU and GPU models, memory, accelerator count, driver and runtime, framework and dependency versions, repository commit, and lockfile or container digest | Environment files and a clean-working-tree record |
| 5. Copyable protocol | Setup commands, evaluation-harness commit, warm-up and repeat rules, batch size, timeouts, and expected output paths | A README that takes a stranger from checkout to the reported tables |
| 6. Randomness and repeats | Every seed and stochastic component, number of independent runs, per-run results, and aggregation rule | Seed manifest and raw results for each run |
| 7. Uncertainty and tests | Confidence interval or justified alternative, resampling unit, and paired or nonparametric test when claiming an improvement | Analysis code and machine-readable statistics |
| 8. Artifact bundle | Runnable code, preprocessing, permitted data or acquisition instructions, configs, seeds, dependencies, result files, plots, and licensing | Versioned repository or archive with expected outputs |
| 9. AI-assisted steps | Judge or assistant version, prompts, system instructions where releasable, raw outputs, parsing, human review, and validation checks | An evidence trail that another person can audit |
| 10. Validity limits | Sampling bias, contamination, construct validity, subgroup coverage, metric gaming, judge reliability, external-validity limits, and compute or access constraints | A limitations section tied to the claims actually made |
1. Define the measurement target before running the model
State the task and population
Name the task precisely: for example, code-generation pass rate on a specified benchmark revision, not simply “coding ability.” Define the population represented by the test items and the population to which you intend to generalize. State hypotheses in advance, including the direction of an expected difference and the comparison that would change a decision.
Define inclusion and exclusion rules
Specify which examples, languages, modalities, users, or model outputs enter the analysis. Explain how malformed, timed-out, refused, or missing outputs are handled. If exclusions are made after inspecting results, label them as post hoc and report their effect rather than silently changing the denominator.
Make every metric interpretable
Give the mathematical or operational definition of each metric, its aggregation level, and whether higher or lower is better. Explain why it answers the decision question. AAAI asks for formal metric descriptions and motivation; NAACL requires a clear definition of the evaluation measure or statistics. If a metric combines components, publish the weights and any normalization.
2. Pin the data so the test set cannot drift
Identify the exact revision
Record the dataset name, release or revision, retrieval date, content hash when available, license, provenance, and train, validation, and test split identifiers. A moving benchmark URL is not a version. If the publisher does not provide immutable releases, preserve an allowed snapshot or checksum and describe how a reader can obtain the same content.
Describe every transformation
Document parsing, normalization, tokenization, filtering, deduplication, truncation, label mapping, and any generated examples. Include script names and commits. State whether preprocessing was applied before or after the split; applying deduplication or filtering across all splits can leak information.
Check contamination and leakage
Describe searches for training-set overlap, benchmark answers in prompts or retrieved context, and accidental test access through caches or tools. Record the method, threshold, and findings. A “clean” claim without a defined check is not reproducible.
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Handle restricted data honestly
When redistribution is prohibited, provide the access procedure, license terms, expected file layout, and a checksum or permitted metadata snapshot. State which parts a reader cannot independently obtain and how that limitation affects the claim.
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Models and checkpoints
Record the model family, exact checkpoint or release identifier, tokenizer version, and any fine-tuning or adapter weights. For a proprietary API, record the endpoint, model name as returned by the service, retrieval date, region if relevant, and all request parameters. “Model X” without a version is not an identifiable system.
Prompts, tools, and stopping
Publish the complete prompt or template, role and system messages where releasable, demonstrations, tool definitions, retrieval corpus revision, context limits, stop sequences, retry policy, and output parsing. State whether prompts were identical across systems and whether a judge saw model names or outputs in a randomized order.
Decoding and hyperparameters
Record temperature, top-p or equivalent sampling controls, maximum tokens, beam settings, random seed handling, batch size, and every final hyperparameter. Distinguish defaults from values explicitly set by the experiment.
4. Freeze the execution environment
Publish the operating system, CPU and GPU model, memory, accelerator count, driver and runtime versions, framework versions, dependency lockfile, container digest if used, and repository commit. Include hardware topology when it can affect numerical behavior. AAAI explicitly asks authors to report computing infrastructure.
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Before a release, verify a clean working tree and record the command’s output or commit identifier. A local uncommitted patch can explain a result that no reader can reproduce.
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5. Make the run protocol copyable
- Start from a clean checkout. Record the repository commit and verify that the working tree is clean.
- Create the declared environment. Use the published lockfile or container digest; do not rely on an undocumented global installation.
- Acquire and verify data. Follow the access instructions, check the supplied hash, and run the preprocessing script named in the manifest.
- Load the exact configuration. Use a versioned file containing model, prompt, decoding, batch, timeout, and metric settings.
- Run warm-up and evaluation as specified. State whether warm-up calls are discarded, how retries work, and how timeouts are counted.
- Persist raw outputs. Save per-example predictions, errors, latency or cost fields when relevant, seed, and protocol version before aggregation.
- Run the analysis script. Generate the summary table, uncertainty intervals, significance tests, and plots from the raw result file rather than manual spreadsheet edits.
- Compare generated files with the report. The README should name expected paths and explain any permitted nondeterministic differences.
A minimal command sequence can be shown in the README, adapted to the project’s actual files:
git clone <repository> && cd <repository>
git checkout <commit>
python -m venv .venv
. .venv/bin/activate
pip install --require-hashes -r requirements.lock
python scripts/fetch_data.py --manifest configs/data.yaml
python scripts/evaluate.py --config configs/run.yaml --seed-manifest configs/seeds.yaml
python scripts/analyze.py --input results/raw.jsonl --output results/summary.csvReplace each angle-bracket value with a real, versioned value in the published project. The important property is that no setup, flag, or output path is implied but undocumented.
6. Treat randomness as part of the measurement
List every stochastic component
Seeds may affect sampling, data shuffling, augmentation, retrieval, judge order, parallel workers, and framework kernels. Record each seed, not only a headline seed. For sampled generation, publish temperature and related controls alongside the seed.
Use independent repeated runs
Report the number of independent runs and the per-run scores. Aggregate with a stated mean or median and show the spread. Do not call repeated examples within one run “independent runs” unless the statistical unit justifies it.
Persist a seed manifest
A machine-readable manifest should map run identifiers to seeds and protocol versions. Keep it with raw outputs so a reader can connect every summary value to its originating run.
7. Report uncertainty and statistical tests
Show intervals, not only point estimates
Publish confidence intervals or another justified uncertainty estimate, name the confidence level, and state the resampling unit. HumanEval’s methodology summary uses a 100-times bootstrap and a 95% confidence interval; if you use a different procedure, explain why and provide the code.
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Choose the right comparison test
When two systems are evaluated on the same items, use a paired test that respects item-level pairing; for non-normal or ordinal outcomes, a nonparametric alternative may be appropriate. State the test, null hypothesis, alternative, handling of ties or missing outputs, and any correction for multiple comparisons. A claim that one model “improves” another should not rest on a difference of point estimates alone.
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Publish the complete result layout
Your result file should contain one row per run and enough fields to reconstruct the report. A publication-ready schema looks like this:
run_id seed benchmark_revision protocol_version n_examples score ci_method ci_level ci_low ci_high
<run_1> <seed_1> <revision> <protocol> <count> <score> <method> 0.95 <low> <high>
<run_2> <seed_2> <revision> <protocol> <count> <score> <method> 0.95 <low> <high>The angle-bracket entries are schema fields, not values to publish. Replace them with measured values in the released CSV or JSON.
Why this is often missing
A 2025 review by the Stanford Institute for Human-Centered Artificial Intelligence found that 14 of 24 prominent benchmarks failed to report statistical significance, and 17 of 24 lacked scripts for result replication. Treat those omissions as a warning: a leaderboard can look precise while leaving both uncertainty and rerun mechanics unspecified.
8. Release an artifact bundle that can be audited
The minimum useful bundle contains:
- Source code and preprocessing scripts at a tagged commit.
- Permitted data, or acquisition instructions plus checksums and licensing terms.
- Configuration files for every reported system and split.
- Seed and run manifests.
- Dependency lockfiles, environment specifications, and container metadata where applicable.
- Raw per-example outputs and aggregated CSV or JSON results.
- Analysis and plotting scripts that regenerate every reported figure and table.
- A README with setup, commands, expected outputs, hardware assumptions, and troubleshooting for access failures.
- A license describing what readers may run, modify, and redistribute.
If a row cannot be filled truthfully, document the gap and its effect on the claim. Do not replace missing provenance with an assertion that the result is “fully reproducible.”
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For an LLM judge or an AI-assisted coding or analysis step, preserve the model and version, endpoint or retrieval date, exact prompts, system instructions where releasable, tool definitions, raw responses, parser and tie-breaking rules, and human review notes. Include validation checks such as agreement with a labeled subset, failure-rate audits, and examples of outputs that the parser rejected.
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The World Bank guidance notes: “Since large language models are non-deterministic, running the same prompt twice may produce different outputs.” Exact byte-for-byte reruns may therefore be impossible. The defensible target is an auditable procedure and evidence trail: the same prompt and protocol, recorded raw outputs, a declared aggregation rule, and an uncertainty analysis that reflects judge variability.
10. State validity limits next to the claim
Sampling and coverage
Explain who or what is absent from the benchmark, whether item difficulty is balanced, and which languages, domains, or subgroups are underrepresented. A test set drawn from one source cannot automatically support a population-wide claim.
Contamination and gaming
Discuss known or plausible training overlap, benchmark memorization, prompt leakage, and ways a system could optimize the metric without improving the intended capability.
Metric and judge reliability
Describe construct validity, inter-rater or judge agreement where applicable, sensitivity to formatting, and known blind spots. A metric can be reproducible yet systematically reward an undesirable behavior.
Resources and access
Report compute budget, accelerator constraints, API availability, rate limits, timeout policies, and any system that could not be evaluated under identical conditions. These constraints affect fairness and the scope of the comparison.
How to compare two benchmark results without fooling yourself
Align all of the following before interpreting a difference:
| Comparison axis | What must match or be reconciled | Reason to stop and investigate |
|---|---|---|
| Benchmark and data | Same benchmark revision, dataset snapshot, split, preprocessing, and contamination policy | A revision or split change can alter item difficulty and coverage |
| Task and population | Same task definition, eligible examples, languages, domains, and subgroup rules | A broader or narrower population changes what the score means |
| Prompt and decoding | Prompt/template, demonstrations, tools, temperature, stopping, retries, and output parser | Different prompting can dominate a model difference |
| Metric and aggregation | Metric definition, direction, denominator, aggregation, and treatment of failures | Scores using different denominators are not directly comparable |
| Runs and uncertainty | Number of runs, seeds, per-run results, interval method, and significance test | Overlapping uncertainty or unreported variation weakens an improvement claim |
| Baseline implementation | Checkpoint, tokenizer, code commit, hyperparameters, and post-processing | An outdated or altered baseline can manufacture an apparent gain |
| Model/API version | Exact release, endpoint, retrieval date, and service settings | Provider updates can change outputs without a paper revision |
| Compute budget | Training or inference budget, hardware, batch, timeouts, and retry allowance | Unequal resources can explain a result attributed to model quality |
Do not compare scores when these conditions differ materially unless the report explicitly frames the result as a different experiment and explains the limitation.
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A practical reporting sequence
- Write the measurement target and hypotheses before inspecting comparative results.
- Create the data manifest and run contamination and leakage checks.
- Freeze model, prompt, tools, decoding, environment, and repository commit.
- Define the seed manifest, repeat policy, timeout behavior, and raw-output format.
- Run every system under the same declared protocol and retain per-example evidence.
- Generate uncertainty intervals and paired or nonparametric tests from scripts.
- Regenerate tables and plots from machine-readable results in a clean environment.
- Publish the artifact bundle, access limitations, and validity boundaries with the headline score.
This sequence makes the score the final product of a documented experiment rather than an isolated number detached from its conditions.
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