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:

Dimension	ML-culture reviewer asks	Systems-culture reviewer asks
Contribution	Is the idea new relative to the ML literature?	Is there a reusable mechanism/abstraction, or just engineering?
Evidence	Are comparisons fair, seeds varied, quality preserved?	Is the workload realistic? Where are the bottleneck analysis and tails?
Skepticism trigger	Accuracy deltas without significance	"Up to Nx" speedups without workload context
Appendix habits	May check math and extra ablations	Rarely reads it; judges the 10 pages

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• Baseline strength: comparing against an untuned or outdated system is treated as invalidating, not just weakening, the result — the field's baselines (serving engines, compilers, training frameworks) improve monthly.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>