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 ACL author response inside an ACL Rolling Review cycle on OpenReview, covering the response window before meta-review, reviewer discussion dynamics, score-change strategy, flagging review issues to the area chair, anonymity rules, and deciding between responding now versus revising for a later ARR cycle.
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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 when ARR reviews land. The response is written inside the review cycle, before the meta-review is finalized — its primary reader is the area chair who will summarize your paper for whatever conference you later commit to. Reopen aclrollingreview.org/reviewing for the current cycle's exact window and rules.
| Objection | Underlying worry | Response that works at ACL |
|---|---|---|
| "Only evaluated on English" | Overclaimed generality | Scope the claim explicitly; cite the multilingual appendix run if one exists |
| "Gains may be contamination" | Test data in pretraining | Point to the decontamination check or concede and describe the planned audit |
| "No error analysis" | Numbers without understanding | Quote the analysis section or summarize 2-3 systematic error classes from logged outputs |
| "Human eval lacks agreement stats" | Unreliable judgments | Report the IAA already computed; never invent a number in the response box |
| "Missing recent baseline X" | Stale comparison set | Distinguish X technically, or accept it as revision work with a stated plan |
We thank Reviewer X. Main point first: [one-sentence resolution].
1. [Factual correction] The model is trained only on the training split
(Section 3.2, line refs); Table 2 reports held-out results.
2. [Clarification] The claim is scoped to the five languages tested
(Section 5); we will tighten the abstract wording.
3. [Evidence pointer] Seeds, prompts, and compute are in Appendix C and
checklist items C1-C3.
4. [Deferred work] A comparison to X requires retraining; we commit to it
in a revision and note why it should not change the ranking.
[Response stance] clarify-in-cycle / concede-and-revise / mixed
[Meta-review target] <sentence you want the AC to write>
[Per-reviewer plan] <R1/R2/R3: main point + evidence anchor>
[Review-issue flags] <deficient-review reports to AC, if any>
[Deferred to resubmission] <experiments promised for a later cycle>
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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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