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To reproduce an AI paper’s result, choose one specific claim, trace it to the paper’s code, data, weights and evaluation instructions, then rerun the experiment under comparable conditions and report what matched—and what did not. Running a repository is not, by itself, evidence that the paper’s result was reproduced.
Choose one result to check
Start with a named item: a result in a table, a figure, a benchmark, an ablation or, where relevant, a theorem. Avoid a promise to reproduce an entire paper until you know which experiments its public materials cover.
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Translate the claim into an observable target: the metric, dataset and split, evaluation procedure, and any tolerance or variation the paper reports. This makes it possible to tell whether your outcome is comparable, rather than merely similar-looking.
Reproducibility is generally about obtaining similar results using the same code and data when available. Pineau et al.’s peer-reviewed 2021 report describes it as a necessary step in verifying the reliability of findings, and discusses a program involving a code submission policy, a community challenge and a checklist: JMLR report.
#1 Best Overall
Find the artifacts and define what is available
Follow links from the paper to its repository, supplementary files, dataset, pretrained weights or checkpoint, and any hosted model or detailed run instructions. Check that the repository corresponds to the paper and, when available, use a release tag or commit associated with the publication. A current repository may have changed since the paper was written.
Make an inventory before running anything:
- Code: Is the implementation public, and does it cover the chosen experiment?
- Data: Is the relevant dataset available, in the stated version and split?
- Weights: Are model weights or checkpoints needed, and are they accessible?
- Instructions: Are the command, environment and evaluation procedure specified?
- Coverage: Which experiments are covered, and which are not?
NeurIPS guidance calls for code, data, exact command and environment instructions, and a statement of experiment coverage. It also recognizes that contribution-appropriate verification may use detailed instructions, hosted models or checkpoints, and that releasing code or data may not be possible. So, no public code does not automatically mean there is no way to check a claim; it does mean you should state which verification route is available. See the NeurIPS Paper Checklist.
Rank #2
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Reconstruct the experiment before you run it
Details can be scattered across the paper, appendix, repository and supplementary material. Record the setup in one place so you can compare your run with the published one.
- Operating system and relevant library, language and framework versions.
- Hardware and memory assumptions, plus any stated compute type or amount.
- Dataset source, version, split and preprocessing.
- Model architecture and the exact checkpoint or weights, if used.
- Training and evaluation commands, configuration and hyperparameters.
- How hyperparameters were selected, the random-seed procedure and number of runs.
- The reported metric and evaluation protocol.
The NeurIPS checklist says, “The instructions should contain the exact command and environment needed to run to reproduce the results.” The checklist, NeurIPS Ethics Guidelines and AAAI-25 Paper Checklist emphasize different aspects of setup and disclosure. These are venue-specific guidance, not proof that every publisher imposes identical requirements.
Check rights, provenance and execution risk
Read the code and dataset licenses and terms before downloading, modifying or redistributing artifacts. Identify creators and versions, and look for stated limitations. When data involves people or sensitive information, check availability restrictions and any stated collection, consent or privacy conditions. NeurIPS ethics guidance addresses dataset licenses, artifact licenses and limitations, representation, and privacy-preserving distribution: NeurIPS Ethics Guidelines.
Treat unfamiliar code as untrusted. NeurIPS 2026 Evaluations and Datasets reviewer guidance recommends running submitted code inside a Docker container, a virtual machine or a network-isolated cloud instance: NeurIPS 2026 E&D Reviewer Guidelines. This is a venue’s safety recommendation, not a guarantee that any one isolation method makes execution safe.
Rank #4
Run the documented experiment and keep an audit trail
- Follow the project’s setup instructions. Use the documented environment and command as closely as practical. If an instruction is missing or no longer works, note the gap rather than silently substituting a different setup.
- Preserve the run details. Save the command, configuration, environment versions, seed procedure, run count, logs and outputs.
- Record every deviation. If you change a dependency, preprocessing step, hyperparameter, checkpoint or evaluation command, write down what changed and why.
- Do not tune toward the headline score without disclosure. If you make changes to get a run working, distinguish the modified experiment from the published configuration.
The checklists emphasize exact environments, clear experiment coverage and suitable statistical reporting; keeping these records makes it possible for someone else to understand what you actually ran. A successful launch shows that some code ran. It does not establish that the paper’s stated experiment was reproduced.
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Compare your output with the paper’s stated target using the same metric, data split and evaluation protocol. A similar score from a different split, preprocessing pipeline or evaluation method is not automatically a reproduction of that experiment. If the artifacts let you run only part of the study, identify that subset precisely.
Best Value
For results affected by stochastic training or repeated sampling, report the number of runs and an appropriate measure of variability, such as error bars, confidence intervals or a suitable significance analysis. No single statistical test or uncertainty measure fits every experiment; choose one suited to the claim and describe what it represents. The NeurIPS and AAAI checklists discuss reporting experiment details and uncertainty: NeurIPS Paper Checklist and AAAI-25 Paper Checklist.
Bit-for-bit equality is not a universal expectation. Results can depend on available artifacts and experimental conditions, so describe the degree of agreement against the paper’s own reported result and uncertainty rather than claiming exact identity without evidence.
State the outcome and its limits
Use a conclusion a reader can audit:
- Reproduced: You ran the stated experiment with the relevant artifacts and comparable setup, and the outcome was similar under the paper’s metric and protocol.
- Partially reproduced: You checked only specified experiments or had to change a material part of the setup. Name the coverage and deviations.
- Could not be checked: A required dataset, checkpoint, instruction, permission or practical resource was unavailable, or an unresolved blocker prevented a meaningful comparison.
Distinguish reproduction using the paper’s code and data from an independent reimplementation. The latter may be valuable, but it tests whether a separate implementation can obtain a similar result, not whether the original artifact reproduces its reported experiment. A checklist can reveal missing information; it cannot provide proprietary data, weights or compute that are not available.
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