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acmmm-reproducibility

Use when strengthening the reproducibility of an ACM MM (ACM Multimedia) paper or preparing for the ACM MM Reproducibility track and ACM artifact badging — capturing environments, media/data access, seeds, and multimodal pipelines so an independent reviewer can rebuild results and reach Artifacts Evaluated or Results Reproduced badges.

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ACM MM Reproducibility

Use this to make an ACM Multimedia result rebuildable — both for main-track credibility and for the dedicated Reproducibility track, which routes artifacts through ACM's badging pipeline. Multimedia adds a wrinkle: the data is often video, audio, or interactive media, and "run the code" is not enough if a reviewer cannot obtain or render the media.

What reproducibility means here

ACM's artifact model distinguishes availability, evaluation, and reproduction. Map your goal to the badge you are actually pursuing:

Badge (ACM terminology)What it assertsWhat you must ship
Artifacts AvailableThe artifact is publicly, permanently retrievableA DOI/archived repository with the code and media pointers
Artifacts Evaluated (Functional/Reusable)Reviewers ran it and it works / is reusableBuild + run instructions, environment, documentation
Results ReproducedAn independent team reproduced the paper's resultsA pipeline that regenerates the reported numbers/media

Confirm the exact badge set offered for the current cycle on the Reproducibility-track call; ACM's badge names and criteria evolve.

The multimodal reproducibility ledger

Keep a single record that ties each reported result to the code, data, and config that produced it:

result: Table 2, row "full model"
  code commit: <hash>
  config: configs/full.yaml
  data: <dataset name + version + anonymous mirror for review>
  media preprocessing: <fps, sample rate, caption source>
  seed(s): <list>
  hardware: <GPU/CPU, hours>
  expected output: results/table2_full.json

Media and data access

  • Provide an anonymous, working path to the data during double-blind review — a mirror that a reviewer can actually download, not a placeholder.
  • State the license and any consent/usage terms; user-generated media often cannot be redistributed, so document how a reviewer obtains it.
  • Pin preprocessing: frame rate, resampling, transcription source, and alignment — small differences here silently break multimodal results.

Determinism where it is achievable

  • Fix and log seeds; note where nondeterminism is irreducible (e.g., some GPU kernels) and report variance instead of pretending to bit-exactness.
  • Version the environment (container or lockfile) and record hardware, since media models are often memory- and throughput-sensitive.

Reproducibility-track readiness pass

  • The Reproducibility and Open Source tracks are single-blind, so the artifact carries its real identity — but the main-track review artifact must still be anonymous.
  • Package for a stranger: a reviewer with your README and nothing else should build, run, and hit an expected-output check within a bounded time.
  • Include a smoke check (see ../../resources/code/README.md) that verifies structure and media rendering before you submit.

Where multimodal pipelines silently break

Multimedia reproduction fails in places pure-code reproduction does not:

  • Codec and container drift — a video re-encoded with a different codec changes pixel values and breaks frame-exact results; pin the decode path.
  • Sample-rate and resampling — audio resampled by a different library shifts features; record the exact resampler and rate.
  • Caption/transcript source — if captions come from an ASR system or a platform, name the version; a different transcript is a different input.
  • Frame sampling — "every k-th frame" depends on the container's frame rate; state fps and the sampling rule.

A reproduction package that omits these looks complete but regenerates different numbers, which is worse than an honest gap.

Anonymous review vs. public artifact

The review artifact and the release artifact have different rules, and conflating them causes anonymity leaks or dead links:

  • During review (double-blind tracks): anonymous repository, anonymous data mirror, no author names in code comments, media metadata, or commit history.
  • At release (camera-ready): the public, de-anonymized repository with a permanent archive (DOI), the license, and the final media — replacing, not merely supplementing, the anonymous mirror.

Output format

[Badge target] Available / Evaluated / Results Reproduced
[Ledger] complete / gaps: <which results lack a trace>
[Data access] anonymous + licensed / broken or unlicensed
[Media preprocessing] pinned / underspecified
[Determinism] seeds+env logged / gaps
[Track blinding] correct for chosen track / mismatch
[Top fixes] <ordered>

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같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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