Skills Artificial Intelligence Neurips Artifact Review Guidelines

Neurips Artifact Review Guidelines

v20260724
neurips-artifact-evaluation
Provides comprehensive guidelines for packaging and presenting research artifacts (code, data, models, benchmarks) submitted to NeurIPS. It details requirements for anonymous review packages, public release, and ensures scientific claims are reproducible by providing necessary metadata, licenses, and execution details.
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Overview

NeurIPS Artifact Evaluation

NeurIPS main track does not reduce artifact quality to a generic badge workflow. It expects code, data, and execution details when they are needed to support the scientific claim, and its checklist and code/data guidance make artifact quality visible to reviewers.

Artifact decision

  • If the contribution is a method, include training and evaluation code or justify why it cannot be shared.
  • If the contribution is a dataset or benchmark, provide metadata, license, preservation plan, representative-use discussion, and access restrictions.
  • If the contribution depends on a model, include weights, prompts, decoding settings, compute resources, or a precise explanation of unavailable components.
  • If the contribution is theoretical, artifact focus may shift to proof checks, symbolic scripts, experiment notebooks, or counterexample generation.

Anonymous review package

  • Keep the ZIP within the current official size limit and anonymize filenames, repository URLs, usernames, commit history, model cards, dataset cards, comments, notebooks, and logs.
  • Include a short README with exact commands, environment, expected runtime, hardware assumptions, and which experiments are reproducible from the package.
  • Do not require reviewers to run unsafe code outside a secure environment.
  • Avoid external links unless the current policy allows them and anonymous browsing is guaranteed.

Public release package

  • De-anonymize accepted artifacts.
  • Add licenses for code, data, model weights, and generated outputs.
  • Archive code in a durable service when appropriate; NeurIPS MLRC guidance recommends Software Heritage for reproducibility papers.
  • Keep a mapping from paper claims to commands or notebooks so users can reproduce headline results.

Output format

[Artifact role] method / dataset / benchmark / model / demo / proof / none
[Review package] sufficient / insufficient
[Anonymity risks] <paths, metadata, URLs, usernames>
[Reproducibility gaps] <commands, environment, data, hardware, licenses>
[Public-release plan] <archive, DOI, license, docs>
Info
Name neurips-artifact-evaluation
Version v20260724
Size 2.32KB
Updated At 2026-07-28
Language