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 ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.
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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 before submission and again before the artifact-evaluation deadline. Reopen the current CFP and call for artifacts to confirm what availability statement and packaging CCS expects this cycle.
| Claim type | What full sharing looks like | Honest fallback when sharing is blocked |
|---|---|---|
| Exploit against deployed software | Runnable PoC plus target build | Redacted PoC, disclosed-and-patched note, synthetic target |
| Defense with overhead numbers | Instrumented build and benchmark scripts | Binaries plus measurement scripts if source is proprietary |
| Internet-scale measurement | Dataset plus collection tooling | Aggregated data with subject-privacy justification for the rest |
| Cryptographic protocol | Reference implementation and test vectors | Spec plus test vectors if the implementation is embargoed |
Claiming an artifact is unavailable without a reason CCS accepts (a license, a disclosure embargo, subject safety) reads as evasion; state the specific reason and offer the closest shareable substitute.
Consider a study scanning the Internet for a misconfiguration. Its reproducibility spine: the scan methodology and rate-limiting, the classification rule for "vulnerable," the ground-truth sample validated by hand, the ethics of scanning and notification, and an aggregated dataset that preserves the finding without exposing individual vulnerable hosts to opportunistic attackers.
For CCS, aim turnkey for anything you submit to artifact evaluation, and state the achieved level honestly rather than overpromising a one-command reproduction that fails on a clean host.
[Claim inventory] <claim -> evidence location>
[Availability posture] full / partial / justified-withheld
[Reproducibility gaps] <versions / configs / budgets / provenance / ethics>
[Paper fixes] <must appear in main PDF or appendix>
[Artifact fixes] <packaging additions before the AE deadline>
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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.
中文简介正在准备中。当前显示原文说明。
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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