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 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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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 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.
ACM's artifact model distinguishes availability, evaluation, and reproduction. Map your goal to the badge you are actually pursuing:
| Badge (ACM terminology) | What it asserts | What you must ship |
|---|---|---|
| Artifacts Available | The artifact is publicly, permanently retrievable | A DOI/archived repository with the code and media pointers |
| Artifacts Evaluated (Functional/Reusable) | Reviewers ran it and it works / is reusable | Build + run instructions, environment, documentation |
| Results Reproduced | An independent team reproduced the paper's results | A 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.
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
../../resources/code/README.md)
that verifies structure and media rendering before you submit.Multimedia reproduction fails in places pure-code reproduction does not:
A reproduction package that omits these looks complete but regenerates different numbers, which is worse than an honest gap.
The review artifact and the release artifact have different rules, and conflating them causes anonymity leaks or dead links:
[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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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 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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