# MLSys 2026 review form

> 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.

- Venue: Conference on Machine Learning and Systems
- Form status: 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.
- Official source: https://mlsys.org/Conferences/2026/CallForPapers
- Overall score: Overall Merit, 1–5
- Rough acceptance bar (our simulation): mean ≥ 3
- Web page: https://reviewpaper.ai/venues/mlsys

## How reviewers read the 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.

## Reviewer form, field by field

### 1. Paper Summary

Type: free text.

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

### 2. Strengths

Type: list of points.

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

### 3. Weaknesses

Type: list of points.

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

### 4. Questions For The Authors

Type: list of points.

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

### 5. Novelty

Type: score, 1–4.

Relative to prior systems and ML work.

- **4**: Surprisingly new contribution
- **3**: New contribution
- **2**: Incremental improvement
- **1**: Published before

### 6. Overall Merit

Type: score, 1–5.

Overall recommendation weighing novelty, quality, impact and interest.

- **5**: Strong accept
- **4**: Accept
- **3**: Weak accept
- **2**: Weak reject
- **1**: Reject

### 7. Reviewer Expertise

Type: score, 1–4.

Your familiarity with the area.

- **4**: Expert
- **3**: Knowledgeable
- **2**: Some familiarity
- **1**: No familiarity

## Area chair meta-review

The MLSys meta-review form is not public.

Recommendation options: Accept · Reject

## MLSys submission requirements

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

## LLM policy

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.

Official policy: https://mlsys.org/Conferences/2026/CallForPapers

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Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=mlsys (3 free reviews). Sample report: https://reviewpaper.ai/sample.
reviewpaper.ai is for authors checking their own unpublished drafts. If you are an assigned reviewer, follow your venue's LLM policy: most venues forbid putting a submission you are reviewing into any external AI tool. Not affiliated with any conference.