ijcai-artifact-evaluation

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Use when packaging IJCAI or IJCAI-ECAI code, data, proofs, models, and appendices as reproducibility evidence or supplementary material, especially when there is no separate artifact-evaluation badge but reviewers need convincing, anonymous, deadline-safe evidence.

brycewang-stanford By brycewang-stanford schedule Updated 6/10/2026

name: ijcai-artifact-evaluation description: Use when packaging IJCAI or IJCAI-ECAI code, data, proofs, models, and appendices as reproducibility evidence or supplementary material, especially when there is no separate artifact-evaluation badge but reviewers need convincing, anonymous, deadline-safe evidence.

IJCAI Artifact Evaluation

Use this for artifact packaging around IJCAI. Treat the current reproducibility guidelines and supplementary-material rules as controlling; do not assume a separate formal artifact evaluation track unless the current cycle announces one.

Package design

  • Decide what reviewers need to classify the results as convincing or credible: proofs, pseudocode, datasets, code, model cards, logs, environment details, or ablation notebooks.
  • Keep essential evidence in the paper whenever space allows because reviewers are not required to read supplementary material.
  • Put optional evidence in the Technical Appendix or ZIP, respecting the current size and format limit. IJCAI-ECAI 2026 allowed up to 50MB in PDF or ZIP form.
  • Anonymize repository paths, user names, institutions, license headers, model checkpoints, data provenance, and notebook metadata.
  • Include a minimal run map: environment, dependencies, hardware, commands, expected outputs, runtime, seeds, and known limitations.
  • For proprietary or restricted data/code, explain why it cannot be shared and provide enough detail for in-principle reproduction.

Evidence by claim type

IJCAI usually has no separate artifact-evaluation badge, so the artifact's job is to move the reproducibility rating toward convincing and to pre-empt a broad PC's doubt. Match the package to the claim.

Claim Artifact that convinces an IJCAI reviewer Weak substitute to avoid
New search/planning algorithm Runnable solver, instance generator, seeds, time/memory limits A results CSV with no way to regenerate it
Theoretical guarantee Full proofs, assumption list, citations to formal tools "Proof omitted for space" with no appendix
Multi-agent protocol Simulator, opponent policies, randomization seeds Screenshots of one run
Learning result Code, environment file, configs, compute and runtime Final-number table only
Dataset Datasheet, license, controlled-access path Vague "available on request"

Worked vignette: packaging a SAT-solver paper

A SAT/CSP paper claims a new restart heuristic wins on hard industrial instances. Strong package: the solver binary or source, the exact instance set or a deterministic generator, solver and compiler versions, per-instance runtimes with the timeout stated, and a run map showing one command that reproduces the cactus plot. Anonymize the repository name, license headers, and any cluster paths. This lets a skeptical constraint-reasoning reviewer re-run a sample and raises the rating from credible to convincing without needing a badge track.

Reviewer pushback and the venue-specific fix

  • "Cannot regenerate the benchmark instances." Ship a generator plus seeds, not just outputs.
  • "Artifact leaks author identity." Scrub paths, license headers, checkpoint names, and notebook metadata before the full-paper deadline; there is no late re-upload.
  • "Restricted data blocks reproduction." Document the legal barrier and give enough protocol for in-principle reproduction rather than implying a release you cannot deliver.

Output format

[Artifact role] paper evidence / supplement / post-acceptance release
[Contents] <proofs/code/data/models/logs/docs>
[Anonymity risks] <paths/licenses/metadata/URLs>
[Reproducibility claim] convincing / credible / weak
[Fixes before upload] <ordered list>
Install via CLI
npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills --skill ijcai-artifact-evaluation
Repository Details
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