AAAI Review Process

Use this to plan around AAAI review rather than treating it as a generic OpenReview rebuttal. Reopen the current review-process page and author FAQ before advising on timing or strategy.

Process model

• AAAI main technical track uses double-blind reviewing.
• AAAI-26 used a two-phase review process. Phase 1 had human reviews and a non-decisional AI-generated review. Papers with sufficiently negative reviews could be rejected before author feedback.
• Papers continuing to Phase 2 received additional reviews and one author feedback phase.
• Final decisions were made through reviewer discussion and senior program committee oversight, not by the AI review.
• Author feedback is short and constrained; it is mainly for correcting misunderstandings, not replacing the paper.

Author strategy

• Reduce Phase 1 reject risk before submission by making contribution, evidence, and checklist compliance obvious.
• When reviews arrive, distinguish human-review claims, AI-review errors, and AC/SPC decision questions.
• Use rebuttal to resolve the highest-impact factual issue under the character limit.
• Do not attack the AI review. Correct it when it contains consequential false statements.
• Avoid new experiments in rebuttal; use submitted evidence and camera-ready promises sparingly.

Stage-by-stage decision map

AAAI's pipeline differs from a single-round OpenReview venue, so plan actions per stage rather than treating every signal as a rebuttal opportunity.

Why papers die in Phase 1

Because the reviewer pool is large and submission volume is high, clearly-below-bar papers are cut early to protect later effort. Common triggers: an unreadable first page, a contribution a non-specialist cannot place, a checklist that contradicts the paper, or evidence too thin to trust. None of these can be repaired after the Phase-1 cut, so they must be eliminated before submission.

Worked vignette

An NLP paper with strong results buries its contribution under three pages of setup. A Phase-1 reviewer from a planning background cannot find the AI claim and scores it a reject; the paper never reaches feedback. The fix belongs entirely pre-submission: a first-page contribution statement and a checklist that matches the experiments, so the broad-AI reviewer can place and trust it fast.

Output format

[Stage] pre-submission / Phase 1 / Phase 2 / rebuttal / discussion / decision
[Decision risk] summary reject / borderline / likely accept / ethics-policy risk
[Best action] revise before submission / rebut / clarify evidence / escalate
[AI-review handling] ignore / correct / cite submitted evidence
[Rationale] <why this fits AAAI process>