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.
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Use when reasoning about the ACM MM (ACM Multimedia) review pipeline — thematic-area routing to reviewers and area chairs, the OpenReview double-blind process and its single-blind track exceptions, the optional anonymous rebuttal, the meta-review and decision, and the oral/poster and award tiers, and where an author actually has leverage.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Use this to build an accurate mental model of how an ACM Multimedia paper is judged, so strategy targets the points where an author can move the outcome and not the points where they cannot.
| Stage | Author leverage | Reality |
|---|---|---|
| Thematic-area choice | High | You pick who reviews you — choose the area that can judge the contribution |
| Submission quality | High | The paper is the main lever; the rebuttal only patches |
| Rebuttal | Medium | Fix factual errors and add small confirmatory results; rarely flips a strong reject |
| Discussion | Low/indirect | You cannot see it; a clean rebuttal gives the AC ammunition |
| Decision/tiers | None directly | Set by AC/PC after discussion |
The main track and Brave New Ideas are double-blind; the Reproducibility, Open Source Software, and Dataset tracks are single-blind because the artifact carries its identity. Confidentiality runs both ways: reviewers must not deanonymize authors, and authors must not try to identify or contact reviewers.
CAN: correct misreadings, add a promised small experiment/ablation, clarify scope, concede narrowly
CANNOT: add new external links, change the contribution, add pages, argue the reviewer is unqualified
The pipeline has a long middle: reviews and rebuttal cluster in late spring, and the decision lands in early July (2026: rebuttal around June 4, notification early July). The strategic consequence is that the confirmatory experiments a reviewer will ask for should be ready before reviews arrive — the rebuttal window is too short to start a new user study or a large ablation from scratch.
[Area routing] well-matched / mismatched (off-topic review risk)
[Review split] factual-fixable: <list> | judgment: <list>
[Decisive critique] <usually the cross-modal/evidence line>
[Rebuttal leverage] high / medium / low
[Next action] <what to fix vs. what to accept>
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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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
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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.
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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.
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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.
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