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 a model — a full theory paper's argument or the structural/conceptual scaffolding behind an empirical AER: Insights short-format manuscript — needs to be made minimal, so it carries the single insight in a few pages. Keeps only what the insight requires; it does not develop a general framework.
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AER: Insights rewards a single, surprising, clean theoretical or modeling insight delivered with the smallest model that produces it. The question is never "is the framework general?" but "what is the most economical model whose one result is important and surprising?" Generality, extensions, and robustness of the modeling go to the Supplemental Appendix; the main text states the assumptions, the one key result, and the intuition. A theory paper here is short and pointed, the opposite of a foundational framework paper for AER or a field journal.
aeri-identification Branch C); keep solution/estimation detail in the appendix.| In the main text (few words) | Supplemental Appendix |
|---|---|
| Setup + key assumptions | Proofs (if long), lemmas, regularity conditions |
| The one headline result + intuition | Generalizations, extensions, alternative specifications |
| The one identifying restriction / counterfactual | Robustness of the model, additional counterfactuals |
The model competes with identification, result, and discussion for the same ≤7,000-word (minus exhibits)
budget (aeri-writing-style). A useful rule: the in-text model should be
sketchable on a single page — assumptions, the one result, the intuition. If the derivation cannot be
sketched that compactly, the full version belongs in the Supplemental Appendix with only the statement and
intuition in-text. A theorem environment plus a one-paragraph proof sketch is often the right in-text
footprint for a theory short paper.
A fictional theory paper proves that, under a mild condition, certification fee structure is irrelevant to information transmission. The AER: Insights version states the model in half a page (a sender, a certifier, two fee regimes), states the irrelevance result, and gives the one-paragraph intuition (the certifier's incentive constraint is unchanged by who pays). The full proof, the regularity conditions, and two generalizations go to the Supplemental Appendix. The surprise — that a policy lever people assume matters does not — is what makes it a single-insight AER: Insights paper rather than a general mechanism-design framework for a field journal.
【Case】theory-is-the-paper / model-supports-empirics
【Headline result/object】<one theorem / restriction / counterfactual>
【Minimal assumptions】<the few that matter; substantive vs technical>
【Intuition】one paragraph
【To the appendix】proofs / extensions / generalizations / robustness
【Next step】aeri-robustness
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。