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 ACL submission against the NLP literature, covering ACL Anthology citation practice, arXiv-versus-published version citation, concurrent LLM-era preprints, prior-cycle ARR resubmission overlap, anonymity-preserving self-citation, and the fast-moving baseline problem in computational linguistics.
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 positioning and citation hygiene. NLP moves fast enough that related-work sections age between ARR cycles; the section must prove the authors know the field as of this cycle, not as of the project's start.
| Contribution type | The contrast reviewers want spelled out |
|---|---|
| Method | Nearest prior method + the mechanism-level difference, not just scores |
| Resource | Existing datasets' coverage gaps: languages, domains, phenomena, license |
| Analysis / evaluation | What prior evaluations measured incorrectly or could not see |
| Metric | Failure cases of the incumbent metric, with correlation evidence |
| Theme-track paper | How the work answers the year's theme question (ACL 2026: explainability) |
One paragraph of typed contrast per lane beats an undifferentiated citation inventory; ACL's compressed format cannot afford taxonomy for its own sake.
Pass 1 (coverage): for each claim of novelty, list the 3 nearest works
from Anthology search + recent cycles; verify each is cited and
contrasted, not just listed.
Pass 2 (hygiene): for each bib entry, check venue-of-record vs arXiv,
Findings attribution, third-person self-citation, and that no cited
item is reviewer-inaccessible.
For each literature lane, three sentences: (1) what the lane established, with its two or three best citations; (2) the specific gap or assumption it left; (3) how this paper differs mechanically. Delete any sentence that neither cites nor contrasts. In a short paper, the whole section may be one such paragraph plus contrasts woven into the introduction — that is normal and reviewers at ACL expect it.
[Positioning verdict] sharp / inventory-like / stale / overlapping
[Nearest neighbors] <3 works -> stated distinction each>
[Freshness risks] <recent baselines or datasets unaddressed>
[Citation hygiene] <arXiv-vs-Anthology, Findings credit, self-citation voice>
[Overlap declarations] <resubmission / workshop / preprint items to declare>
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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.