本文へ移動
cccskills
無料GitHub で公開

acmmm-review-process

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.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.4 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

ACM MM Review Process

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.

The pipeline

  1. Submission + thematic area. The paper enters OpenReview under a chosen thematic area, which routes it to a matching reviewer pool and area chair.
  2. Assignment. Reviewers and an AC are assigned; a mismatched thematic area is where papers get reviewers who cannot judge the contribution.
  3. Reviews. Reviewers assess novelty, cross-modal soundness, evidence (including user studies where relevant), and reproducibility.
  4. Rebuttal. An optional, anonymous author response addresses reviews; new external links are not allowed.
  5. Discussion + meta-review. Reviewers and the AC discuss; the AC writes a meta-review and recommendation.
  6. Decision + tiers. Accept/reject, with accepted papers sorted into presentation tiers (oral vs. poster) and award consideration.

Where leverage exists

StageAuthor leverageReality
Thematic-area choiceHighYou pick who reviews you — choose the area that can judge the contribution
Submission qualityHighThe paper is the main lever; the rebuttal only patches
RebuttalMediumFix factual errors and add small confirmatory results; rarely flips a strong reject
DiscussionLow/indirectYou cannot see it; a clean rebuttal gives the AC ammunition
Decision/tiersNone directlySet by AC/PC after discussion

Reading a review set

  • Separate factual errors (a reviewer misread a result) from judgment (they find the delta small); the first is fixable in rebuttal, the second usually is not.
  • Weight the cross-modal critiques: "the fusion is not shown to matter" is often the decisive line, and an ablation is the answer.
  • Note the AC's implicit questions in the meta-review; that is who the rebuttal is really written for.

Double-blind and its exceptions

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.

What the rebuttal cannot do

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

Reading scores and the meta-review

  • A spread of scores (one champion, one detractor) is normal; the rebuttal targets the detractor's concrete objection and gives the champion and AC something to cite.
  • A uniform lukewarm set is harder than a split — there is no champion to convert, so the rebuttal must move a shared concern, usually the cross-modal-significance one.
  • Weight the AC's meta-review most: it is the synthesis the decision rests on, and its implicit questions are what your response should answer.

The dates that constrain strategy

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.

Confidentiality and conduct

  • Treat all reviews and discussion as confidential.
  • Do not attempt to deanonymize reviewers or lobby ACs outside the system.
  • Report suspected violations through the official channel, not by public posting.

Output format

[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>

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

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

brycewang-stanford のスキルをすべて見る

このスキルの問題を報告する