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Acmmm Review Process

CommunityPopular
brycewang-stanford
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.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacmmm-review-process
Stars
1.2K
Forks
159
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Acmmm Review Process AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-review-process
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git /tmp/Awesome-Journal-Skills
mkdir -p .claude/skills
cp -r /tmp/Awesome-Journal-Skills/ACM-MM-Skills/skills/acmmm-review-process .claude/skills/acmmm-review-process
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acmmm Review Process in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Acmmm Review Process on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Acmmm Review Process is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

text
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

text
[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>

Frequently asked questions

What does the Acmmm Review Process AI skill do?

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.

Why use Acmmm Review Process on TypingMind?

Because you install it once and use it with any model. Acmmm Review Process is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Acmmm Review Process in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-review-process. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acmmm Review Process?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Acmmm Review Process?

As many as you like. As long as a model supports skills, you can use Acmmm Review Process with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Acmmm Review Process AI skill free?

Yes. It is published on GitHub by brycewang-stanford under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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