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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when deciding which robustness checks belong in the few in-text words of an American Economic Review: Insights (AER: Insights) short-format manuscript versus the online Supplemental Appendix. Triages checks against the length cap; it does not design the identification (see aeri-identification).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
A standard paper answers every threat in-text; AER: Insights cannot. With ≤7,000 words (minus 200 per exhibit, ≤5 exhibits), robustness must be triaged: keep in-text only the one or two checks that, if they failed, would kill the headline result, and move everything else to the Supplemental Appendix (which has no length cap but is read only by skeptics and the data editor). The skill is deciding the minimum credible set of in-text checks and signposting the rest in a single sentence.
For each candidate check, ask: "If a referee saw only the main text, would the absence of this check make them disbelieve the headline result?"
| Answer | Placement |
|---|---|
| Yes — it defends the core identifying assumption | In-text (1–2 max), often a single robustness exhibit or a sentence |
| No — it reassures but the result survives without seeing it | Supplemental Appendix, named in one in-text sentence |
| It is really an extension / new question | Appendix or a different paper, not "robustness" |
Use one sentence to point to the appendix: "The result is robust to [list] (Supplemental Appendix Section X; Tables X1–X6)." This buys credibility for the price of a sentence and keeps the word budget for the insight. Reproduce nothing in-text that the sentence already covers.
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. AER: Insights is a short format built around one decisive result, so the body/appendix split is even tighter — run the design cleanly the first time.
romano_wolf (step-down FWER, accounts for
cross-test correlation) or benjamini_hochberg — report the adjusted threshold.oster_delta / sensemakr — the confounder strength that would
overturn the headline.wild_cluster_bootstrap (few clusters), twoway_cluster / conley.audit_result(result_id) lists the missing checks and the
exact suggest_function for each — no guessing the battery.etable / did_summary_to_latex from the handle — no retyped numbers.Keep the decisive checks in the body and the exhaustive (now actually-run) battery in the appendix. See the executed chain in the JF execution walkthrough.
【Core-assumption check (in-text)】<the 1–2 that must stay>
【Signpost sentence】"Robust to [list] (Suppl. Appendix §X, Tables …)."
【To the appendix】alt bandwidths / forms / placebos / heterogeneity / clustering
【Exhibit budget】in-text robustness uses __ of 5 exhibits
【Next step】aeri-tables-figures
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.