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 building or trimming the exhibits of an American Economic Review: Insights (AER: Insights) short-format manuscript to the hard five-exhibit budget, one page each, no significance asterisks. Designs the minimum exhibit set; it does not run the analysis behind them.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Choose the exhibit set that, together, tells the single-insight story. A common allocation for an empirical short paper:
| Slot | Typical content |
|---|---|
| 1 | The headline result — the one figure or table a reader remembers (event-study, RD plot, treatment-effect estimate) |
| 2 | The central identification diagnostic (pre-trend leads, density, first-stage, balance) |
| 3 | A mechanism or key heterogeneity exhibit, if it is part of the single insight |
| 4–5 | Held in reserve — a robustness summary or a descriptive table only if load-bearing |
If two exhibits can merge into one well-designed multi-panel page, merge them and reclaim a slot (and 200 words). Everything else goes to the Supplemental Appendix.
Generate exhibits from the fitted result, not by retyping numbers (the usual source of
body-vs-appendix drift). Full map: execution-with-mcp.
etable (multi-model columns) or did_summary_to_latex straight from the
result_id — one variable definition, one set of numbers, body and appendix in sync.plot_from_result / enhanced_event_study_plot / event_study_table —
axis units and the SE/clustering note baked in.See a full fitted-result → exhibit chain in the JF execution walkthrough.
【Exhibit count】__ / 5 (each ≤1 page?)
【Word reconciliation】7000 − 200×__ = __ words available; draft = __ words
【Exhibit 1 (headline)】<self-reading result>
【Exhibit 2 (identification)】<diagnostic>
【Exhibits 3–5】<mechanism / robustness / descriptive — only if load-bearing>
【No-asterisk check】SEs / CIs in notes? [Y/N]
【To the appendix】<exhibits demoted>
【Next step】aeri-writing-style
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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.