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This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

This guide provides comprehensive instructions for packaging multiagent system (MARL) and game theory evidence for academic submissions, particularly for conferences like AAMAS. It emphasizes that the artifact must be inspectable, containing not just a trained model, but the game definition, environment code, opponent sets, and interaction protocols, ensuring rigorous reproducibility and reviewability.

A comprehensive guide for packaging and submitting evidence packages (artifacts) for NLP research papers, such as code, datasets, prompts, and model outputs. It details requirements for reproducibility, adhering to the Responsible NLP checklist, ensuring proper anonymization, and preparing for post-acceptance public release in academic settings like ACL.

A comprehensive guide for authors on best practices for packaging, documenting, and ensuring the reproducibility of LLM artifacts (weights, data, code, prompts, etc.). It addresses requirements for anonymous academic review and public post-acceptance release, focusing on reproducibility, licensing, and transparency.

This guide instructs authors on how to package theoretical proofs for submission to COLT (Conference on Learning Theory). Since COLT does not have a formal artifact track, it emphasizes treating the proof appendix as the primary 'artifact.' It details best practices for creating a verifiable proof ledger, rigorously tracking constants and external results, and managing supplementary code to ensure maximum academic credibility.

This comprehensive guide outlines best practices for packaging and releasing scientific artifacts—including code, training configurations, demonstration data, and policy checkpoints—for robotics research papers. It details how to prepare materials for anonymous reviewer assessment and for durable public release, ensuring the work is fully reproducible by the academic community.

A comprehensive guide for authors submitting research to CVPR, detailing best practices for packaging code, models, and datasets. It covers requirements for review-time reproducibility (sealed-box rules) and long-term public release (licensing, provenance, and version pinning), ensuring that the work is runnable by any skeptical third party.

A comprehensive guide to preparing accountability artifacts for AI research papers, particularly for venues like FAccT. It covers the necessary documentation genres—including datasheets for datasets, model cards for models, and impact assessments—to ensure transparency, verifiability, and accountability regarding claims of fairness and harm mitigation in AI systems. The focus is on making research outputs rigorously traceable and ethically responsible.

This guide details the best practices for packaging, documenting, and releasing research artifacts (code, models, data) for top-tier ML conferences. It covers two stages: preparing a non-identifiable archive for anonymous review, and managing the public release runway, focusing on reproducibility, licensing, and sustained maintenance.

This guide provides essential instructions for authors submitting research to the ECAI conference, focusing on creating credible and reproducible artifacts. It strongly cautions authors about misconceptions regarding separate artifact badges, emphasizing that credibility must be built into the paper and supplement for direct reviewer judgment. Guidelines are provided for matching the artifact (code, proofs, data) to the specific contribution type (Theory, ML, Optimization).

A comprehensive guide for researchers submitting work to INTERSPEECH. It details how to package and manage various artifacts—including code, audio samples, and trained models—through the conference lifecycle. The document emphasizes maintaining anonymity during review, ensuring scientific reproducibility, managing complex data licenses, and adhering to ethical guidelines for voice data and synthetic media release.

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.