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aamas-camera-ready

Use when preparing an accepted AAMAS paper for the IFAAMAS open-access camera-ready, covering author de-anonymization, proceedings metadata, two-column reflow of game figures and learning curves, resolving rebuttal-stage promises, registration, in-person presentation, and the public code and environment release for a multiagent paper.

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AAMAS Camera Ready

Use this after acceptance. Reopen the current AAMAS camera-ready instructions, the proceedings author kit, the registration page, and the decision notice before advising authors; AAMAS proceedings are published open-access by IFAAMAS.

Camera-ready audit

  • Convert the anonymous submission to the public version: author names, affiliations, acknowledgements, funding, and de-anonymized code, data, and environment links.
  • Apply the current final-page rules and template. Confirm whether the accepted-paper mode changes the page budget from the 8-page submission body, and reflow accordingly.
  • Check proceedings metadata: title, abstract, author order and ORCID, subject areas, bibliography, and any required copyright or release form.
  • Integrate every rebuttal-stage promise without expanding the accepted contribution: a wording fix, a moved assumption, or an added caption line - not a new experiment.
  • Prepare the public artifact: repository, multiagent environments, opponent/population sets, seeds, checkpoints, and an archival citation, now that anonymity no longer applies.
  • Confirm registration and in-person presentation obligations for at least one author.

Anonymous-to-final flip: what to recheck

ElementSubmission (anonymous)Camera-ready (final)
Page oneTracking number, no author blockAuthor names, affiliations, ORCID
Acknowledgements / fundingRemovedRestored
Code / environment linksAnonymized placeholderPublic, licensed, citable archive
Self-citation phrasing"prior work [anon]"Natural first-person, de-anonymized
Page budget8-page bodyConfirm the accepted-paper budget, then reflow

Two-column reflow for multiagent papers

  • Switching the style file from anonymous to final mode changes spacing; recheck every page break, figure placement, and table width after the flip.
  • Extensive-form game trees, payoff matrices, and multi-panel learning curves often need full-width spans; verify nothing is clipped at the column boundary.
  • Material that fit under the tracking number can overflow once the author block and acknowledgements are added; confirm rather than assume the budget absorbs it.
  • Keep theorem, assumption, and game-definition numbering identical to the reviewed version so the public reviews on OpenReview remain traceable to the final text.

De-anonymization sweep

  • Restore the author block, acknowledgements, grants, and contribution statements the anonymous version stripped.
  • Rewrite anonymized self-citations ("prior work [anon]") into natural first-person form.
  • Replace anonymized repository placeholders with the public, licensed, citable archive, and test every link and every environment install from a logged-out machine.

Worked example: honoring a rebuttal promise

The area chair accepted on the condition that the paper add a held-out opponent to the evaluation, which the rebuttal promised. Camera-ready move: add the one opponent, report it with the same seeds and standard errors as the others, and add a sentence to the protocol - without re-tuning the method or re-framing the contribution the reviewers evaluated.

Hedged logistics

  • Registration pricing, copyright-form mechanics, the exact camera-ready date, and the proceedings template can change every cycle; confirm against the decision notice and current author kit.

Output format

[Camera-ready status] Ready / Needs fixes / Blocked
[Final package] <PDF/source/bib/metadata/forms/artifacts>
[Policy checks] <page/authors/presentation/registration/artifact release>
[Rebuttal-promise map] <promise -> final edit>
[Remaining owner] <person -> task>

レビュー

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概要と使いどころ

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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