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Get Started Free →Use when reasoning about how CVPR review actually works at 16,000-submission scale, covering the OpenReview pipeline and timeline, reviewer-duty enforcement and desk rejects, the reviewer LLM ban, AC and discussion dynamics, oral/highlight/poster decision tiers, and calibrating expectations to a ~25% acceptance rate.
.claude/skills/brycewang-stanford-cvpr-review-process/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-18 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 94% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 67% | 0% |
Use this to model what happens to a CVPR paper between upload and decision, and where author leverage exists. Every mechanism below is the 2026 cycle as verified 2026-07-08; scale numbers come from official CVPR program announcements.
The 2026 cycle put 16,092 submissions through full review (withdrawals and desk rejects excluded from that count) and accepted 4,090 — 25.42%. Doing that required roughly doubling the reviewer pool to 25,149 reviewers across 97 countries, coordinated by 909 area chairs. Two consequences follow for authors:
pool doubled in one year; official training material exists for a reason). Write for a competent generalist in vision, not the five people in your subfield.
AC's job easy — clear claims, clean rebuttal, consistent reviews — get the benefit of scarce attention.
| Phase | 2026 dates | What authors can do | |---|---|---| | Profile/abstract registration | Nov 6, 2025 | Fix profiles, finalize author list | | Paper + CRF deadline | Nov 13, 2025 | Submit; after this, nothing | | Supplement deadline | Nov 20, 2025 | Upload depth material | | Reviewing | Dec – Jan 12, 2026 | Serve your own reviewer duties well | | Reviews released | Jan 22, 2026 | Read cold, then triage | | Rebuttal | → Jan 29, 2026 | One page (see cvpr-author-response) | | Reviewer–AC discussion | Jan 30 – Feb 5, 2026 | Nothing — your rebuttal speaks | | Decisions | Feb 20, 2026 | Plan camera-ready or resubmission |
CVPR's response to scale is to make authorship and reviewing one social contract, with teeth that surprised people in recent cycles:
unless exempted; complete OpenReview profiles are mandatory for everyone.
reviews on time, or delivers highly irresponsible ones, can be desk-rejected — all of that person's submissions — at PC discretion. A coauthor's negligence in January can kill your November submission.
API), and sharing substantial paper content with an LLM is prohibited; grammar checkers and background research are the carve-outs. As an author, this means a suspiciously LLM-flavored review is reportable to the AC, not just annoying.
text# Confidential-channel escalation, in order 1. Rebuttal PDF → factual corrections all reviewers see 2. Confidential comment to AC (where the form allows) → reviewer misconduct: LLM-written review, review of the wrong paper, demonstrably unread paper. Cite evidence, stay unemotional. 3. Never → contacting reviewers or ACs outside OpenReview; that is an integrity violation on YOUR side.
The review form's exact fields shift by cycle (待核实 each year), but the durable skeleton a CVPR review argues through is: summary of the paper in the reviewer's own words (misreadings here predict everything downstream), claimed strengths, weaknesses with the score-driving one usually listed first, and a recommendation with confidence. Read your reviews against that skeleton: a wrong summary is rebuttal priority one, because every weakness derived from it inherits the error.
Acceptance is not binary. The 2026 program sorted acceptances into poster, highlight (program-flagged posters, 578 papers per program trackers), and oral (141 papers in four parallel tracks), plus 74 award candidates — treat those tier counts as reported, and the tiering itself as the durable pattern. Tier decisions ride on review scores plus AC/SAC advocacy, which is one more reason the rebuttal's real audience is the AC.
Roughly three of four reviewed papers exit here, so treat rejection processing as part of the process, not an aftermath:
fix before any resubmission; CVF reviewer pools overlap and repeat objections compound), communication failures (a reviewer missed something the paper does say — a writing-style problem, see cvpr-writing-style), and taste (novelty judgments that a different draw of three reviewers may not share).
problem; one champion plus one detractor means the discussion phase decided it, and the rebuttal is where to look for what failed.
ICCV, and ECCV reviewer pools makes "same paper, new lottery ticket" a strategy with memory.
Rejection is the modal outcome for good work; plan the ICCV/ECCV/next-CVPR path at submission time, not in grief.
kills a factual objection, and one reviewer willing to champion. What doesn't: eloquence about importance.
averages, more than authors assume.
in 2026).
text[Stage] pre-submission / in-review / rebuttal / discussion / decided [Process risks] duty-compliance · profile validity · policy exposure [Review read] per-reviewer: score-driver + fixability in one line [AC theory] what the meta-reviewer needs in order to advocate [Next actions] <dated, owner-assigned>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-18 | fail→pass | 25,796 | 19,006 | -26% | 1 | 1 | 0% | 2,869 | 3,535 | +23% | 0 | 0 | — |
case-19 | pass→pass | 15,397 | 20,397 | +32% | 1 | 1 | 0% | 2,509 | 3,869 | +54% | 0 | 0 | — |
case-01 | pass→pass | 20,457 | 20,168 | -1% | 1 | 1 | 0% | 2,507 | 3,628 | +45% | 0 | 0 | — |
case-02 | pass→fail | 21,577 | 16,038 | -26% | 1 | 1 | 0% | 3,221 | 4,419 | +37% | 0 | 0 | — |
case-03 | pass→pass | 22,586 | 20,660 | -9% | 1 | 1 | 0% | 2,628 | 3,942 | +50% | 0 | 0 | — |
case-04 | fail→pass | 27,498 | 19,545 | -29% | 1 | 1 | 0% | 2,717 | 3,800 | +40% | 0 | 0 | — |
case-05 | fail→pass | 15,806 | 20,869 | +32% | 1 | 1 | 0% | 1,903 | 3,694 | +94% | 0 | 0 | — |
case-06 | fail→pass | 22,796 | 13,009 | -43% | 1 | 1 | 0% | 2,304 | 3,327 | +44% | 0 | 0 | — |
case-07 | pass→pass | 20,029 | 22,573 | +13% | 1 | 1 | 0% | 2,366 | 3,758 | +59% | 0 | 0 | — |
case-08 | pass→pass | 17,559 | 18,772 | +7% | 1 | 1 | 0% | 1,998 | 3,279 | +64% | 0 | 0 | — |
case-09 | pass→pass | 15,451 | 11,653 | -25% | 1 | 1 | 0% | 1,655 | 2,667 | +61% | 0 | 0 | — |
case-10 | pass→pass | 23,166 | 19,696 | -15% | 1 | 1 | 0% | 2,560 | 3,778 | +48% | 0 | 0 | — |
case-11 | pass→pass | 19,040 | 17,118 | -10% | 1 | 1 | 0% | 2,090 | 3,499 | +67% | 0 | 0 | — |
case-12 | fail→pass | 20,413 | 21,867 | +7% | 1 | 1 | 0% | 2,575 | 4,313 | +67% | 0 | 0 | — |
case-13 | pass→pass | 18,449 | 17,172 | -7% | 1 | 1 | 0% | 2,029 | 3,527 | +74% | 0 | 0 | — |
case-14 | pass→pass | 18,476 | 18,707 | +1% | 1 | 1 | 0% | 2,045 | 3,623 | +77% | 0 | 0 | — |
case-15 | pass→pass | 17,616 | 16,125 | -8% | 1 | 1 | 0% | 2,313 | 3,497 | +51% | 0 | 0 | — |
case-16 | pass→pass | 13,616 | 18,699 | +37% | 1 | 1 | 0% | 2,104 | 3,607 | +71% | 0 | 0 | — |
case-17 | pass→pass | 16,217 | 13,096 | -19% | 1 | 1 | 0% | 1,694 | 2,863 | +69% | 0 | 0 | — |
case-20 | pass→pass | 10,803 | 15,629 | +45% | 1 | 1 | 0% | 1,700 | 3,132 | +84% | 0 | 0 | — |
case-21 | pass→pass | 19,871 | 8,097 | -59% | 1 | 1 | 0% | 2,214 | 2,917 | +32% | 0 | 0 | — |
case-22 | fail→pass | 19,703 | 12,823 | -35% | 1 | 1 | 0% | 2,673 | 3,601 | +35% | 0 | 0 | — |
case-23 | pass→pass | 14,407 | 16,906 | +17% | 1 | 1 | 0% | 1,848 | 3,136 | +70% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +22 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.