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 revising an American Economic Review: Insights (AER: Insights) short-format manuscript to lead with the result, fit the hard word cap (7,000 words minus 200 per exhibit), and write the ≤100-word abstract. Enforces the length budget and the lead-with-the-result arc; it does not change the analysis.
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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。
Treat the cap as a design constraint from the first draft, not a final trim. Every sentence competes with the insight for space.
Unlike a long paper that builds to its result, an AER: Insights paper states the answer in the first paragraph:
The body then delivers exactly that arc: setup (tight), identification, result, the one or two checks, a short discussion. No second result, no survey, no extended derivations in-text.
aeri-robustness).aeri-literature-positioning).aeri-theory-model).aeri-tables-figures).【First paragraph】states result + magnitude + SE/CI? [Y/N]
【Word count】main text = __ ; cap = 7000 − 200×__ = __ ; under? [Y/N]
【Abstract】__ words (≤100?) and states the result? [Y/N]
【Cut list to hit cap】<secondary results / lit / proofs / exhibits merged>
【No-asterisk check】[Y/N]
【Next step】aeri-replication-package
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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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