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Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -33% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -9% | 0% |
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:
textresult: 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
that a reviewer can actually download, not a placeholder.
redistributed, so document how a reviewer obtains it.
differences here silently break multimodal results.
report variance instead of pretending to bit-exactness.
often memory- and throughput-sensitive.
its real identity — but the main-track review artifact must still be anonymous.
hit an expected-output check within a bounded time.
../../resources/code/README.md)that verifies structure and media rendering before you submit.
Multimedia reproduction fails in places pure-code reproduction does not:
and breaks frame-exact results; pin the decode path.
the exact resampler and rate.
version; a different transcript is a different input.
the sampling rule.
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:
names in code comments, media metadata, or commit history.
(DOI), the license, and the final media — replacing, not merely supplementing, the anonymous mirror.
text[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>
Other measured skills in the registry, with their headline benchmark lift.