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.
日本語の概要は準備中です。原文の説明を表示しています。
Use when an American Economic Journal: Macroeconomics (AEJ: Macro) decision letter has arrived (R&R or reject-with-comments) and you need a response-letter strategy and revision plan. Plans the rebuttal and revision; it does not run new identification, model, or robustness work itself (route to those skills).
インストールする前に、エージェントに与えられる指示の中身を確認できます。
AEJ: Macro review is single-blind and editor/coeditor-managed; the coeditor's letter is the binding instruction set — when a referee and the coeditor disagree, follow the coeditor. The response must show that the broad-interest macro contribution survived the scrutiny and that the identified-empirical and/or quantitative-model objections are answered with new exhibits, not new prose alone. Macro referees reward a response that re-runs the decisive check and reports the (possibly changed) number honestly.
aejmac-identification (re-do the SVAR/LP/narrative defense).aejmac-robustness (sample splits, method cross-check).aejmac-theory-model.aejmac-literature-positioning / aejmac-writing-style.aejmac-replication-package.A referee says the monetary-policy output response is a pre-Volcker artifact. You re-estimate post-1984 (aejmac-robustness) and the peak response falls from -0.8% to -0.6% (90% bands overlapping). The strong response block: "We agree the early sample could drive the result. Re-estimating on 1984–2019 (new Table A4, Figure A3), the peak output response is -0.6% (90% band [-1.0, -0.2]), within the band of the full-sample estimate; the qualitative conclusion is unchanged and the magnitude is now reported for the modern monetary regime. We have revised the abstract and Section 4 accordingly." Honest, exhibit-backed, conclusion preserved and appropriately scoped.
【Decision type】R&R / reject-with-comments
【Triage table built】comment → action → owning skill → location → status? [Y/N]
【Coeditor letter treated as binding】conflicts reconciled to it? [Y/N]
【New exhibits for empirical/model objections】listed? [Y/N]
【Honest number movement reported】? [Y/N / NA]
【Letter style】point-by-point with exact pointers? [Y/N]
【Next step】revise manuscript; re-run aejmac-submission preflight before resubmitting
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。