Use when packaging ACM CCS artifacts for the artifact-evaluation committee and the ACM badges — Artifacts Available, Artifacts Evaluated Functional, Artifacts Evaluated Reusable, and Results Reproduced — covering what security evaluators inspect, how to make attacks and defenses turnkey, and how to justify withheld artifacts.
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Use this for artifact packaging around CCS. CCS runs an optional artifact-evaluation process after acceptance; passing artifacts earn ACM badges that appear on the paper's first page. Reopen the current call for artifacts for the exact badge set, submission form, and deadlines.
| Badge | What it certifies | What the committee does |
|---|---|---|
| Artifacts Available | The artifact is publicly archived with a stable identifier | Confirms the artifact is retrievable and permanent |
| Artifacts Evaluated - Functional | The artifact runs and does what the paper says | Executes it against the documented steps |
| Artifacts Evaluated - Reusable | It exceeds functional quality and others can reuse it | Judges structure, documentation, and reusability |
| Results Reproduced | The main paper results are independently reproduced | Reruns experiments and checks they support the claims |
Available is about archival permanence; Functional and Reusable are about quality; Results Reproduced is the highest bar and requires the committee to regenerate your headline numbers.
| Claim type | First artifact inspected | Common failure caught |
|---|---|---|
| Exploit against real software | The PoC and its target build instructions | Target version unspecified; PoC fails to build |
| Defense with overhead numbers | The benchmark runner and instrumented build | Overhead cannot be reproduced; workload undocumented |
| Measurement study | The collection and classification scripts | Classification rule differs from the paper's prose |
| Cryptographic implementation | Test vectors and the reference build | No test vectors; outputs cannot be checked |
A hypothetical accepted paper presents a new fuzzer that found N bugs. For Reusable and Results Reproduced: ship a container pinning the fuzzer, the target corpus, and the seed set; document one command to reproduce a representative crash and one to rerun a bounded campaign; provide the bug list with disclosure status; and note which crashes are embargoed pending vendor patches, offering redacted reproducers for those.
[Target badges] available / functional / reusable / results-reproduced
[Contents] <code / data / container / test vectors / logs>
[Turnkey level] one-command / scripted / descriptive / weak
[Safe-handling] <gating and disclosure posture for dangerous artifacts>
[Withheld-and-justified] <what and why, with substitute offered>
[Fixes before submission] <ordered list>
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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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