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Get Started Free →Use when packaging the artifacts behind an ACM CHI paper — prototypes, study instruments, codebooks, datasets, analysis code — for anonymous review scrutiny and for post-acceptance archival release, in a venue with no formal artifact-evaluation committee doing it for you.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 12% | 0% |
CHI's Papers track posted no separate artifact-evaluation committee or badge track for the cycles checked (2026, 2027; confirm each year — 待核实 as a standing item). That absence cuts both ways: nobody certifies your artifacts, and nobody but you ensures a skeptical reviewer can inspect them. At CHI the "artifact" is rarely just code — it is the prototype, the study instruments, the codebook, the dataset, and the video evidence of the system working. Package for two audiences with opposite needs: the anonymous reviewer with five minutes, and the future researcher with a real use for your materials.
| Artifact | Reviewer's question | Archival value after acceptance | |---|---|---| | Prototype / system code | "Does the claimed interaction actually exist?" | Others extend or compare against it | | Video figure of real use | "Does it work outside the authors' hands?" | Permanent DL evidence (chi-supplementary) | | Study instruments (guides, questionnaires, tasks) | "Was the study what the paper says?" | Direct reuse in replications | | Codebook / analysis audit trail | "Do the themes live in the data?" | Methods teaching material | | Dataset (de-identified) | "Do the numbers re-derive?" | Secondary analysis | | Design files (3D prints, schematics, figma) | "Could this be rebuilt?" | Fabrication replication | | Prompts / model configs for AI conditions | "What system did participants face?" | The only record once the API moves |
Inventory first, then decide per artifact: reviewed now, released later, or honestly withheld with a reason (chi-reproducibility has the data-sharing ladder).
The review-phase archive rides the single September deadline with everything else. Design it so the first five minutes land:
analysis/h1_test.R →Table 2." Three such lines are worth thirty pages of appendix.
(requirements.txt, renv.lock) because a reviewer will not debug pip.
physical device, the video figure is the reviewable artifact; the archive adds schematics and firmware so the claim is auditable in principle.
.git, no metadata, no named accounts, anonymizedplatform views only, usernames scrubbed from notebook outputs and file paths.
bash# Cold-simulate the reviewer on the exact ZIP you will upload rm -rf /tmp/ae && unzip -q supplement.zip -d /tmp/ae && cd /tmp/ae cat README* | head -30 # does the claims map appear immediately? grep -rEil 'author|university|(^|[^a-z])lab' --include='*.md' . | head time bash run_minimal_demo.sh # the five-minute budget is literal
At the publication-ready stage (February for CHI 2027), rebuild the artifact set under real names for permanence, not for review:
institutional archive — with a DOI; a lab URL is a dead link in five years.
CC BY-NC), and record third-party constraints (stimuli copyrights, model terms of service for cached AI outputs).
and participant re-identification harms real people. Where consent was narrow, release the instruments and codebook instead of the data — respected practice.
onward in a separate branch; future readers need the paper's version, not HEAD.
A packaging note that says "the deployment used partner infrastructure we cannot ship; this archive contains the full client, the API contract, and a mock server reproducing the study conditions" earns more trust than a repo padded with dead code that hides the same gap. Reviewers at CHI read many partial artifacts; what they punish is discovering the gap themselves after the README implied completeness.
own claim-evidence audit (chi-experiments).
chi-submission's checks.the TAPS window are where consent violations happen.
text[Inventory] <artifact: reviewed / released-later / withheld+reason, per row> [Claims map] present in README: yes/no · claims covered: <n>/<n> [Five-minute test] cold demo ran in <time> / failed at <step> [Anonymity] archive clean: yes/no — <channels checked> [Release plan] home: <DL/OSF/Zenodo> · license: <code/data> · consent re-check owner: <name>
Other measured skills in the registry, with their headline benchmark lift.