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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when positioning an ACM CCS submission against the security big-four and specialist literature, including arXiv preprints, prior CCS/S&P/USENIX/NDSS papers, concurrent disclosures, CVE and advisory records, and the attack-and-defense lineage that a SIGSAC program committee expects to see credited.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this to audit novelty and eligibility. Reopen the current CFP for dual-submission, anonymity, and prior-publication rules before advising authors.
| Literature lane | Typical sources | What CCS reviewers check |
|---|---|---|
| Security big four | CCS, IEEE S&P, USENIX Security, NDSS proceedings | Whether the nearest attack/defense is compared or explicitly distinguished |
| Specialist venues | PETS/PoPETs, ESORICS, ACSAC, CRYPTO/EUROCRYPT | Whether the sub-community's known results are acknowledged |
| Practitioner record | CVEs, vendor advisories, CTF and exploit write-ups | Whether a known real-world case predates the claimed novelty |
A bibliography citing only academic papers while ignoring a well-known CVE that already demonstrated the attack is a recognizable CCS reject pattern that no amount of measurement polish repairs.
Imagine the paper reports a new cache side-channel that recovers keys from a TLS library. Its nearest neighbors: an S&P paper with a slower channel, a USENIX defense that assumed the channel was impractical, and a public CVE for a related timing bug. The novelty sentence should name all three contrasts — a faster channel than the S&P line, a break of the USENIX paper's practicality assumption, and a distinct root cause from the CVE.
[Eligibility] clear / needs declaration / risky
[Closest literatures] <big-four / specialist / practitioner record>
[Nearest 3 works] <work -> distinction>
[Archival-overlap risk] <none / issues>
[Novelty sentence] <CCS-ready contribution contrast>
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.