brycewang-stanford/awesome-journal-skills/aaai-artifact-evaluation

AAAI Artifact Evaluation

Use this to prepare artifacts that reviewers can use to assess reproducibility. AAAI supplementary material is part of the submission record; after review starts, do not assume it can be updated.

Artifact package

• Provide a technical appendix for proofs, algorithms, assumptions, hyperparameters, and extended

experiments.

• Provide code/data ZIPs that reproduce main tables or figures, with a short README, environment,

commands, seeds, expected outputs, and runtime.

• Provide multimedia appendices only when they support the technical claim.
• Remove author names, usernames, paths, repository history, cloud buckets, API keys, and metadata.
• Avoid web pointers in the reviewed submission unless current rules explicitly allow them.
• Include licensing and access notes for datasets, models, and third-party code.

AAAI-specific discipline

• Treat the supplementary deadline as final.
• Verify ZIP integrity before submission; missing or corrupted files may not be fixable during

rebuttal.

• Make the reproducibility checklist consistent with the artifact package.
• Prepare a post-acceptance public release path but keep review artifacts anonymous.

What an AAAI reviewer actually opens

AAAI does not run a separate badged artifact-evaluation committee the way some systems venues do; the same broad-AI reviewer who scores the paper also inspects whatever supplement you attach. That reviewer may be a planning, knowledge-representation, or constraint-satisfaction specialist rather than a deep-learning engineer, so the artifact has to be legible without insider tooling. Optimize for a reviewer who skims, not one who will spend an afternoon configuring a cluster.

Phase-1 artifact red flags

Because clearly-below-bar papers can be cut before author feedback, a supplement that looks thin or unrunnable is a cheap reason to summary-reject. Avoid these:

• Checklist promises released code, but the ZIP only holds figures and no scripts.
• A "see our repository" pointer to a mutable, deanonymizing URL.
• Multimedia attached for spectacle that carries no technical claim, inflating size with no rigor.
• Datasets shipped with no license note, leaving reuse legality unverifiable.

Worked vignette

A constraint-solving paper claims a 30% node-expansion reduction. The team ships a large ZIP of raw solver logs but no driver script. The reproduction path is empty, so artifact status is "risky"; the fix is a small run_main.py that regenerates Table 2 from seeds, a trimmed log sample, and a license for the benchmark instances. The raw dump moves to the post-acceptance release.

Output format

[Artifact status] complete / partial / risky / unavailable
[Submitted files] technical appendix / multimedia appendix / code-data ZIP
[Reviewer reproduction path] <commands and expected output>
[Anonymity risks] <metadata, links, paths, logs>
[Missing items] <data, code, seeds, licenses, hardware>