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Mobisys review process

Skill brycewang-stanford/Awesome-Journal-Skills/MobiSys-Skills/skills/mobisys-review-process

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的 Claude Code/Codex 期刊技能包,从选题、识别策略到表格规范与审稿回复全流程,助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill mobisys-review-process

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What its author says it does

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Use when explaining or planning around MobiSys peer review — the two-round process with an early-reject cut after round 1, the round-2 rebuttal window, double-blind HotCRP mechanics, the systems-and-services reviewer pool, and decision criteria for on-device claims, so response strategy fits how MobiSys decides.

SKILL.md

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MobiSys Review Process

Use this to reason about review-stage strategy. Reopen the current CFP, the HotCRP site, and the organizing-committee page before making process claims — the mechanics are cycle-specific.

Process model

  • MobiSys uses HotCRP for submission and review, with a paper-registration step preceding the paper upload and double-blind review (author identities hidden since 2017).
  • Review runs in two rounds. Papers that do not advance past round 1 receive an early rejection with reviews, so authors can re-plan before the process ends. Round-2 survivors reach reviews and a rebuttal window.
  • The rebuttal is scope-limited: correct factual errors and answer specific reviewer questions; new results are admissible only when directly responsive, or may be promised for camera-ready (mobisys-author-response).
  • Unlike MobiCom and NSDI, the rendered 2026 CFP describes accept/reject through the rebuttal, not a separate one-shot revision channel; treat any revision path as 待核实 per cycle.
  • Accepted papers are published in the ACM Digital Library, so camera-ready compliance and artifact badges matter as much as the initial accept.

Who reviews here

  • The pool is systems-and-services researchers: people who build and measure mobile runtimes, on-device ML, sensing services, and platforms, and who read an energy or latency claim as something to be re-derived, not admired.
  • Because MobiSys is specialized, topical matches are close; a hand-wavy device claim or an undefined energy boundary gets caught rather than skimmed past.
  • Borderline mobile-systems papers usually fall on one of three edges: an evaluation on a single device or single run, an energy/thermal claim without a described instrument, or a contribution that is really a better model dressed as a system.

Scoring leverage table

Review dimensionWhat raises itWhat sinks it
ContributionA system mechanism forced by the device constraintA model or algorithm with an incidental phone demo
On-device evidenceDistributions on real hardware across devices/statesSingle-device, single-run, simulation-only numbers
Energy/latency rigorNamed instrument, boundary, and sustained-load behavior"Efficient" with no measured joules or throttle trace
ClarityAn explicit operating point (device, workload, budget)Latency numbers with no device or workload context

Stage-by-stage realism

  • Round 1: triage by what a systems reviewer would weigh — is the device evidence real and is the contribution a system? A thin evaluation is the classic early reject.
  • Rebuttal: windows are short and scope is narrow; a precise reply that fixes a factual error and answers the one decision-critical question beats an exhaustive one.
  • Decision: one unresolved evidence objection (single-device, undefined energy boundary) outweighs several resolved clarity complaints.
  • After accept: artifact evaluation and camera-ready are their own gates (mobisys-artifact-evaluation, mobisys-camera-ready).

Output format

[Current stage] submitted / round-1 / round-2 reviews / rebuttal / decision / camera-ready
[Decision actors] <reviewers / AC / chairs>
[Likely leverage] <contribution / on-device evidence / energy-latency rigor / clarity>
[Forbidden moves] <identity leak / out-of-scope new results in rebuttal>
[Next response move] <one action>

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.