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