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 preparing an accepted ACL main-conference or Findings paper for camera-ready, covering the extra content page, de-anonymization and acknowledgements, AI-assistance disclosure, keeping the Limitations section, ACL Anthology metadata and CC BY 4.0 publication, meta-review-driven edits, and presentation-mode logistics.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Use this after ACL (or Findings of ACL) acceptance. Camera-ready quality controls how the paper looks forever in the ACL Anthology, which is the permanent open-access record. For ACL 2026 the camera-ready deadline was April 19, 2026 — confirm the current edition's date in the acceptance email before planning anything.
| Item | Why it matters at ACL | Common error |
|---|---|---|
| Title/abstract in the form | Becomes the Anthology landing page | Unicode or LaTeX macros pasted raw |
| Author names + order | Permanent citation record, BibTeX for everyone | Name spelled differently than prior papers |
| PDF fonts embedded | Anthology archival requirement | Missing Type-1/TrueType embedding from figures |
| License acceptance | Anthology publishes under CC BY 4.0 (post-2016 policy) | Assuming you can restrict reuse later |
| Video/poster uploads | Linked from the Anthology page when provided | Skipped, losing visibility |
Publication in the Anthology is open access with no author fee; ACL's cost model is conference registration, not APCs.
Work from the meta-review, not memory:
Day 1: meta-review edit checklist; claim-scope audit
Day 2: de-anonymize; acknowledgements incl. funding + AI disclosure
Day 3: public artifact links live; README + license on the repo
Day 4: rebuild with final style; font embedding + page-count check
Day 5: metadata form; abstract matches PDF; submit and archive everything
[Camera-ready status] Ready / Needs fixes / Blocked
[Meta-review edits] <requested change -> edit made>
[Extra-page budget] <what the added space was spent on>
[De-anonymization + disclosure] <authors/acks/AI-use/links status>
[Anthology package] <PDF/metadata/license/artifact links>
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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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。