NeurIPS Reproducibility

Use this skill when a NeurIPS paper's claim depends on experiments, data, code, or a reproducibility argument. The immediate target is a trustworthy main-track paper; the alternative route is MLRC/TMLR when the central contribution is reproduction, replication, or generalizability of prior claims.

Main-track reproducibility bar

• State exact data splits, preprocessing, hyperparameters, selection criteria, compute resources, software versions, and random-seed protocol.
• Report uncertainty where it matters: confidence intervals, standard errors, multiple seeds, sensitivity checks, or negative findings.
• Distinguish exploratory experiments from evidence that supports the main claim.
• Make code/data availability match the checklist answer; "no" is allowed with justification, but a central open-source benchmark or dataset usually needs accessible artifacts.
• For human, private, medical, proprietary, or safety-sensitive data, document access constraints and ethical controls rather than pretending full release is possible.

MLRC route check