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Get Started Free →Use when reasoning about how an ACM CoNEXT submission is evaluated, covering double-anonymous review, the two-round TPC process with online discussion and a TPC meeting, the Accept / Reject / one-shot "major" revision decision categories, the journal-style revise-and-resubmit re-read by the original reviewers, shepherding, and how CoNEXT's two-cycle process differs from SIGCOMM and NSDI.
.claude/skills/brycewang-stanford-conext-review-process/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 1% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 22% | 0% |
Model the pipeline before interpreting any single review. CoNEXT's process is journal-style: papers are PACMNET articles, and the one-shot "major" revision is a first-class decision, not a soft rejection. The most consequential mental shift for authors arriving from a plain accept/reject conference is that a major revision is a genuine revise-and-resubmit round re-read by the same reviewers — closer to a journal R&R — and that you get exactly one shot at it.
mutually hidden through reviewing.
meeting; strong early-round papers advance, and clearly-below-bar papers may receive an early-reject notification partway through the cycle.
revision arrives with a summary of merits and a list of minimum necessary changes; authors get roughly two months to revise, and the same reviewers re-read the revision for a final accept/reject.
who confirms the required changes were made before camera-ready.
camera-ready compliance, and reproducibility follow-through matter as much as the initial verdict.
| Decision | What it means | Author move | |---|---|---| | Accept | Contribution and evidence hold; minor polish only | Camera-ready + (optional) badge; do not reopen scope | | One-shot major revision | Repairable gaps: a missing measurement, an unfair baseline, an unclear claim | Treat as a one-shot R&R: make or explicitly decline every "minimum necessary change," evidenced | | Reject | Structural: wrong evaluation platform, no credible baseline, thin contribution | Reframe or reroute (SIGCOMM/NSDI/IMC/SIGMETRICS or the other cycle), do not lightly resubmit unchanged |
The strategic reading: write the initial submission so that whatever is weakest is fixable inside the revision window (a measurement you can add, a baseline you can tune) rather than structural (an evaluation platform you cannot rebuild in two months). The process rewards repairable papers, and because the revision is one shot, a half-addressed change list is what turns it into a rejection.
identity is the two cycles a year feeding one program and the PACMNET journal framing. Never assume the two share a calendar, template, or revision mechanics.
with its own proceedings; CoNEXT's revision lands in a PACMNET issue. Do not carry NSDI's timing or format across.
alongside systems and architecture work in the same program, and can enter via either cycle.
Expect a panel of networking TPC members matched to your subarea — systems, measurement, architecture, wireless. They look for evidence on the real target platform (a testbed, deployment, or trace, not a simulation standing in for hardware), check whether claims outrun the measurements, ask whether baselines are real and fairly tuned, and often open the artifact. Vague methodology gets caught in the two-round discussion, not skimmed.
text[Before submission] topic tags + cycle choice -> reviewer pool and calendar (largest lever) [Round-1/early] factual corrections, targeted evidence, clarifying misreadings [One-shot revision] the strongest lever: a tracked-change revision + point-by-point, anonymous response letter re-read by the same reviewers, checked by a shepherd [After reject] no appeal; reroute to a sibling venue or the other cycle
A response moves borderline papers when it corrects a factual misreading or supplies a measurement a reviewer said was missing; it does not move papers when it argues taste. In a one-shot revision, silent omissions — a listed minimum-necessary change neither made nor explicitly declined with a reason — are what the second read punishes hardest.
Weight reviews before answering. A review that cites your section numbers, figures, and testbed setup was read closely and will be read closely again in the revision — its author is your likely advocate if the response holds. A review that discusses only novelty has left soundness and reproducibility to the others; answer each reviewer on the axis they raised. The major-revision change list is the scoring rubric for the second read: treat every item as mandatory unless you can justify declining it.
budget the two-month window like a deadline.
evidence for an advocate, not a closing argument.
paper through discussion.
dates; confirm the one you are in.
text[Process stage] pre-submission / awaiting reviews / one-shot revision / final / accepted [Cycle] December / June [Decision category] accept / one-shot major revision / reject, with the criterion driving it [Criterion map] each review point -> significance | soundness | measurement quality | baselines | clarity | reproducibility [Leverage plan] the next-stage action that can actually change the outcome [Forbidden moves] identity leak (incl. in the response letter) / unsupported new claims
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 32,729 | 18,983 | -42% | 1 | 1 | 0% | 4,242 | 3,616 | -15% | 0 | 0 | — |
case-02 | fail→fail | 26,095 | 19,609 | -25% | 1 | 1 | 0% | 3,192 | 3,757 | +18% | 0 | 0 | — |
case-03 | fail→pass | 29,731 | 20,193 | -32% | 1 | 1 | 0% | 3,921 | 3,954 | +1% | 0 | 0 | — |
case-04 | fail→pass | 16,716 | 14,139 | -15% | 1 | 1 | 0% | 2,475 | 2,868 | +16% | 0 | 0 | — |
case-05 | fail→fail | 26,105 | 16,376 | -37% | 1 | 1 | 0% | 3,325 | 3,109 | -6% | 0 | 0 | — |
case-06 | pass→pass | 12,126 | 15,545 | +28% | 1 | 1 | 0% | 1,880 | 3,050 | +62% | 0 | 0 | — |
case-07 | pass→pass | 18,040 | 18,658 | +3% | 1 | 1 | 0% | 2,015 | 3,152 | +56% | 0 | 0 | — |
case-08 | fail→pass | 22,601 | 16,324 | -28% | 1 | 1 | 0% | 2,673 | 3,317 | +24% | 0 | 0 | — |
case-09 | fail→pass | 17,089 | 18,774 | +10% | 1 | 1 | 0% | 2,697 | 3,289 | +22% | 0 | 0 | — |
case-10 | fail→pass | 15,921 | 18,544 | +16% | 1 | 1 | 0% | 1,724 | 3,335 | +93% | 0 | 0 | — |
case-11 | pass→pass | 17,814 | 20,082 | +13% | 1 | 1 | 0% | 2,666 | 3,419 | +28% | 0 | 0 | — |
case-12 | pass→pass | 44,321 | 10,978 | -75% | 1 | 1 | 0% | 1,755 | 3,012 | +72% | 0 | 0 | — |
case-13 | pass→pass | 16,563 | 18,214 | +10% | 1 | 1 | 0% | 2,303 | 3,416 | +48% | 0 | 0 | — |
case-14 | fail→pass | 13,085 | 14,319 | +9% | 1 | 1 | 0% | 1,921 | 2,960 | +54% | 0 | 0 | — |
case-15 | fail→pass | 19,297 | 19,405 | +1% | 1 | 1 | 0% | 2,059 | 3,488 | +69% | 0 | 0 | — |
case-16 | fail→fail | 24,760 | 13,960 | -44% | 1 | 1 | 0% | 2,591 | 3,563 | +38% | 0 | 0 | — |
case-17 | pass→pass | 17,008 | 13,199 | -22% | 1 | 1 | 0% | 1,922 | 3,619 | +88% | 0 | 0 | — |
case-18 | pass→pass | 10,689 | 13,253 | +24% | 1 | 1 | 0% | 1,417 | 2,664 | +88% | 0 | 0 | — |
case-19 | fail→pass | 17,840 | 13,386 | -25% | 1 | 1 | 0% | 1,711 | 2,543 | +49% | 0 | 0 | — |
case-20 | pass→fail | 19,795 | 12,285 | -38% | 1 | 1 | 0% | 1,972 | 3,292 | +67% | 0 | 0 | — |
case-21 | pass→fail | 25,151 | 24,439 | -3% | 1 | 1 | 0% | 2,799 | 4,088 | +46% | 0 | 0 | — |
case-22 | fail→fail | 20,037 | 21,879 | +9% | 1 | 1 | 0% | 2,044 | 3,652 | +79% | 0 | 0 | — |
case-23 | pass→pass | 21,584 | 24,356 | +13% | 1 | 1 | 0% | 2,790 | 4,520 | +62% | 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 +30 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 cases got worse with the skill loaded, and they are 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.