Mlsys Review Process

MLSys Review Process

Use this to model what happens to a Conference on Machine Learning and Systems submission between upload and decision. Mechanics below are 2026-cycle anchors (verified 2026-07-08); the venue is young and still redesigns its process — 2026 alone added an entire track — so reopen the current CFP and OpenReview group before strategic decisions.

The pipeline (2026 anchors)

• Submission via OpenReview (MLSys.org/2026/Conference group) by October 30, 2025.
• Double-blind review; arXiv posting allowed in parallel.
• Reviews released January 12, 2026; author responses due January 16; notifications January 25-26. There is no long discussion phase to rescue a paper — the response is a single, short shot (see mlsys-author-response).
• Accepted papers publish on proceedings.mlsys.org; artifact evaluation follows as a separate, optional, badge-awarding stage (March 8 - April 8 in 2026).

Who reviews here — the two-culture pool

MLSys program committees deliberately mix ML researchers with systems, architecture, and compiler people. The same paper is read through two different quality lenses:

A submission that satisfies only one culture gets a split review set, and split reviews at a single-shot-response venue are dangerous: you have four days to convert one side. Write for both from the start — name the mechanism (systems lens) and show quality is preserved under the optimization (ML lens).

What decisions actually turn on

• Workload representativeness is the most common fatal objection: a system evaluated only on microbenchmarks or toy models loses both cultures at once.
• Baseline strength: comparing against an untuned or outdated system is treated as invalidating, not just weakening, the result — the field's baselines (serving engines, compilers, training frameworks) improve monthly.
• Claim-evidence scope match: a general claim ("for transformer inference") tested on one model family gets scoped down by reviewers if the authors did not scope it first.
• Honesty signals: reported non-wins, stated tradeoffs, and cost accounting raise trust scores disproportionately at this venue.
• Industrial-track papers are judged on different axes: scale realism, design methodology depth, and benchmark detail — explicitly not novelty (2026 track rules). A research-track-style novelty defense in an industrial-track response misses the actual bar, and vice versa; know which rubric your reviewers were given.
• The appendix asymmetry: since reviewers are not obliged to read the separate appendix, an objection already answered there is still a live objection — the review process treats the 10 pages as the paper, and responses must quote the appendix material into the reply rather than pointing at it indignantly.

Reading a review packet

Triage grid for an MLSys review set:
  R1 (systems): workload not representative        -> decision-critical, answerable
  R2 (ML):      missing significance on Table 2     -> decision-critical, cheap to fix
  R3 (systems): "wish you compared against X"       -> check X's publication date vs
                                                       your deadline; if after, say so
  All:          writing nits                        -> batch into two lines
Rank by (decision impact) x (answerability in 4 days); ignore tone.

Meta-review synthesis rewards responses that resolve the shared objection across reviewers; three reviewers independently doubting the baseline is one problem, not three.

Reading scores and reviewer signals

• A short review with a middling score from a systems reviewer usually means "plausible but I don't trust the evaluation" — the response should add measurement, not prose.
• A long, detailed negative review is often the most convertible: the reviewer engaged deeply enough to change their mind if the specific objections close.
• Confidence scores matter more here than at mega-conferences: with small, topically close panels, a high-confidence negative reviewer who is factually wrong is the highest-priority target, because the meta-reviewer will otherwise defer to them.
• Watch for the culture split masquerading as disagreement: R1 (ML) at accept and R3 (systems) at reject with non-overlapping objections is not noise — it means the paper currently serves one audience. Say explicitly in the response how each culture's concern is met.
• Do not read tone as signal; systems-review bluntness ("this evaluation is not credible") is genre convention, not a verdict on the idea.

Confidentiality and conduct

• Submissions are confidential to the review process; reviewers must not use or share them. Authors likewise must not fish for reviewer identities or contact PC members about their paper outside the platform.
• The double-blind-plus-arXiv model means a reviewer may recognize your preprint; policy treats good-faith anonymization by authors as the requirement, not reviewer ignorance. Do not exploit this by advertising the arXiv version at reviewers.

After the decision

• Rejected: MLSys reviews are unusually actionable (workload, baseline, and measurement gaps are concrete); the annual-cycle question is whether to strengthen for next MLSys or reroute to a systems venue with a nearer deadline — see mlsys-topic-selection.
• Accepted: review strategy hands off to camera-ready reconciliation and the artifact stage, where a different committee re-examines your evidence in executable form.

Cycle-volatility warnings

• Response-window length, discussion mechanics, reviewer-volunteer expectations for authors, and any AI-use policy in reviewing were not verifiable for 2026 beyond the dates above (待核实) — confirm on the live pages.
• Acceptance-rate folklore changes yearly and is omitted here deliberately.

Output format

[Stage] pre-submission / under review / response / decided
[Review-set shape] <systems vs ML objections, split or aligned>
[Decision-critical objection] <the one the meta-review will weigh>
[Response leverage] <answerable in window? with what evidence>
[Conduct checks] <anonymity/contact/confidentiality risks>
[Next move] <one action>

Source: brycewang-stanford/Awesome-Journal-Skills → MLSys-Skills/skills/mlsys-review-process/SKILL.md