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 Advanced Materials manuscript must be fit to its article-type format — Communication versus Research Article — where word count and display items are budgeted together against a typeset page limit. Manages the length budget; does not restructure the science or polish prose line by line.
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。
Advanced Materials counts length in typeset published pages, not manuscript pages — a double-spaced 25-page manuscript may typeset to far fewer pages in the two-column layout. Word count and display items (figures, schemes, tables) count together against the page budget. Approximate targets (verify current limits on the author page):
| Article type | Typeset budget (approx.) | Abstract | Display items (approx.) |
|---|---|---|---|
| Communication | ~4 published pages (~3500 words) | none — opening paragraph acts as abstract | ~4 figures/tables |
| Research Article | ~10 published pages (~3000–8000 words) | short abstract (~200 words) + 3–7 keywords | ~3–8 display items |
| Review / Progress | longer, by invitation/agreement | structured per type | many |
A large or multi-panel figure consumes a disproportionate slice of the page budget: adding a panel can cost a paragraph of text.
Trim only after the central advance, characterization, and figures are stable, so you cut padding, not evidence:
advmat-writing-style).advmat-supplementary).Never trim: the decisive characterization, the benchmarking, or the headline metric's statistics.
【Article type】Communication / Research Article — matches content? yes / reconsider
【Typeset page estimate】within budget? yes / over by ~X
【Display items】counted with text? N figures + M tables
【Trim done】hype / prose / secondary-data-to-SI / figures / refs
【Untouched】characterization + benchmarking + statistics intact? yes
【Next】advmat-cover-letter or advmat-submission
Page limits, word counts, abstract/keyword rules, and display-item allowances differ by article type and change — verify all on the official Wiley Advanced Materials author page.
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。