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ModeledConference on Machine Learning and Systems

The MLSys 2026 review form, explained

Simulate MLSys-style reviews: summary, strengths, weaknesses, novelty, technical quality and impact of the ML-systems contribution, questions, an overall merit score and reviewer expertise.

Modeled, not official. MLSys 2026 reviewed in a private HotCRP form (MLSys 2027 moves to OpenReview) and publishes no reviewer guidelines. The official criteria are novelty, quality, impact and interest; the fields and scales here follow common systems-conference (HotCRP) review forms and are not official.

MLSys 2027 paper deadlineOct 30, 2026 · 08:00 AoE–d ––h ––m
Review my paper in MLSys format Official source
Overall score
Overall Merit, 1–5
Sub-scores
Novelty (1–4)
Confidence
1–4
Form fields
7
Author checklist
3 items

How MLSys reviewers read your paper

MLSys research-track papers are judged on a combination of novelty, quality, impact and interest at the intersection of ML and systems; the industry track instead stresses impact, technical quality, clarity and lessons from real-world systems with no novelty requirement. Reviews are double-blind with an author response before decisions.

  • Judge both the ML and the systems sides: realistic workloads, hardware and baselines matter.
  • Ask where the gains come from (breakdowns, ablations) rather than only whether they exist.
  • Specific and constructive; the appendix is optional reading.

Where the bar sits. MLSys 2026 published about 135 papers; acceptance rates are not published (historically ~20–25%). On this modeled 1–5 scale a mean of ≥ 3 (Weak accept) is a rough proxy for acceptance.

The reviewer form, field by field

In the order reviewers see it, with every score and its official wording.

  1. 01

    Paper Summary

    Free text

    The system or technique, the workloads it targets, and the claimed gains.

  2. 02

    Strengths

    List of points

    Novelty, quality and impact: new system design or insight, realistic workloads and hardware, strong baselines, meaningful end-to-end gains.

  3. 03

    Weaknesses

    List of points

    Missing or weak baselines, unrealistic setups, unexplained overheads, limited generality, missing ablations or breakdowns of where the gains come from.

  4. 04

    Questions For The Authors

    List of points

    Questions for the author response whose answers could change your evaluation.

  5. 05

    Novelty

    Score · 1–4

    Relative to prior systems and ML work.

    ScoreMeaning
    4Surprisingly new contribution
    3New contribution
    2Incremental improvement
    1Published before
  6. 06

    Overall Merit

    Score · 1–5

    Overall recommendation weighing novelty, quality, impact and interest.

    1. 5Strong accept
    2. 4Accept
    3. 3Weak accept
    4. 2Weak reject
    5. 1Reject
  7. 07

    Reviewer Expertise

    Score · 1–4

    Your familiarity with the area.

    1. 4Expert
    2. 3Knowledgeable
    3. 2Some familiarity
    4. 1No familiarity

Area chair meta-review

The MLSys meta-review form is not public.

  • Accept
  • Reject

MLSys submission requirements

Reviewers check these. Gaps are easy points to lose.

  1. Up to 10 pages two-column plus references; appendix separate (reviewers need not read it)
  2. Double-blind (industry track hides author names only)
  3. Artifact evaluation is voluntary, after acceptance, and does not affect the decision

Policy on LLMs in reviewing

No MLSys reviewer LLM policy is published. Authors may not submit papers whose content is generated by LLMs alone (unless illustrative or part of the experiments). Treat submissions as confidential and do not upload them to LLMs.

Read the official policy

Author mode is for your own unpublished work. If you are an assigned reviewer, follow these rules: you are responsible for your review.

See your paper through MLSys reviewers' eyes

Three simulated reviewers fill in exactly this form for your draft, then an area chair writes the meta-review. Every point links to the passage it's about. Your first three runs are free.

Review my paper — 3 free

reviewpaper.ai is not affiliated with MLSys or its organizers. Forms change between cycles; always check the official source.