Skills Artificial Intelligence Strengthening AI Research Reproducibility Evidence

Strengthening AI Research Reproducibility Evidence

v20260724
ijcai-reproducibility
This guide provides detailed best practices for ensuring the reproducibility of advanced AI research across various domains, including algorithms, theories, multi-agent systems, and datasets. It outlines the specific evidence required—from pseudocode and seeds to full proofs and public datasets—to elevate a submission's credibility rating from 'irreproducible' to 'convincing' for top-tier AI conferences.
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Overview

IJCAI Reproducibility

Use this before submission and again before camera-ready. Reopen the current reproducibility page; IJCAI's rubric and checklist can change.

Evidence map

  • Target at least a credible reproducibility rating for every major result; aim for convincing when resources can be shared safely.
  • For new algorithms, include a conceptual outline or pseudocode in the paper.
  • For theory, state assumptions, formal claims, citations to tools, proof sketches, and proofs for novel claims.
  • For datasets, cite existing sources, describe unavailable or proprietary datasets, and include or promise release of new datasets only when legally possible.
  • For computational experiments, report final hyperparameters, search ranges, selection criteria, seeds or repeat strategy, software versions, compute infrastructure, and runtime.
  • Add an optional "Reproducibility" section when it resolves likely reviewer doubts, but do not depend on supplementary material for central claims.

Rubric mapping by contribution shape

IJCAI's rating moves from irreproducible to credible to convincing. Because the PC spans symbolic, search, planning, KR, multi-agent, and learning work, the evidence that earns "convincing" differs by contribution. Map each result to the right column before drafting.

Contribution Minimum for "credible" What lifts it to "convincing"
New algorithm (search/planning/CSP) Pseudocode plus complexity claim in body Runnable code, instance generator, seeds, version pins
Theoretical result Assumptions and proof sketch in body Full appendix proofs, citations to formal tools
Multi-agent / game-theoretic Protocol, agent counts, payoffs Released simulator, opponent policies, seeds
Dataset contribution Source, licensing, collection described Public release or controlled-access path, datasheet
Learning experiment Hyperparameters and splits in body Code, environment file, compute, repeat strategy

Worked vignette: a multi-agent coordination paper

A coordination-protocol paper claims faster convergence but reports no seeds and ships no simulator. To reach "convincing": state the agent count sweep and payoff matrix in the body; appendix the convergence proof sketch; release an anonymized simulator with fixed seeds and the opponent policies; pin library versions. Without the simulator the result stays "credible" at best, which an IJCAI reviewer may flag as a significance discount.

Reviewer pushback and the venue-specific fix

  • "Cannot tell if the proof holds." Put assumptions and a sketch in the body and full proofs in the Technical Appendix; do not bury the theorem statement.
  • "No way to regenerate the instances." Ship a deterministic generator and seeds, not just result tables, since IJCAI search/planning reviewers re-run when skeptical.
  • "Dataset is unavailable." Explain the legal barrier and give enough detail for in-principle reproduction; never imply a release you cannot deliver by camera-ready.
  • "Reproducibility rests on the appendix." Pull the load-bearing protocol into the 7-page body because reviewers are not required to open the supplement.

Output format

[Result inventory] <claim -> evidence location>
[Rubric target] convincing / credible / currently weak
[Missing details] <algorithm/theory/data/compute/hyperparameters/seeds>
[Paper fixes] <must be in main PDF>
[Supplement fixes] <optional or supporting evidence>
Info
Name ijcai-reproducibility
Version v20260724
Size 3.71KB
Updated At 2026-07-28
Language