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 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.
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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 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.
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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