# SIGIR 2026 review form

> Simulate SIGIR 2026 full-paper reviews: relevance to the conference, Relevance / Novelty / Technical Soundness / Presentation ratings, Strengths, Weaknesses, an Overall Recommendation (−3 to +3) with justification, and confidence.

- Venue: ACM SIGIR Conference on Research and Development in Information Retrieval
- Form status: modeled, not official. SIGIR does not publish its review form. Field order and the values seen (e.g. "-1 poor" … "2 excellent", "1 weak accept", "0 borderline") come from public SIGIR 2026 Short Papers reviews on OpenReview; the Full Papers form is assumed to match, and the unseen ends of the overall and confidence scales are ours.
- Official source: https://sigir2026.org/en-AU/pages/submissions/full-papers-track
- Overall score: Overall Recommendation Score, -3–3
- Rough acceptance bar (our simulation): mean ≥ 1
- Web page: https://reviewpaper.ai/venues/sigir

## How reviewers read the paper

SIGIR 2026 moved to OpenReview. Each paper gets at least three PC reviews and a senior PC meta-review; decisions weigh novelty, technical depth, elegance, practical or theoretical impact, and presentation. Reviewers rate relevance, novelty, technical soundness and presentation, list strengths and weaknesses, and justify an overall recommendation. Generic, empty, insulting, late or LLM-generated reviews are "highly irresponsible" and can lead to sanctions.

- Fair, thoughtful and respectful; never generic or empty.
- Check experimental rigor explicitly: baselines, statistical significance, generalizability, reproducibility.
- Back "done before" claims with citations.

**Where the bar sits.** SIGIR full papers are historically accepted at about 20–21%; 2026 figures are not published. On the −3…3 scale a mean of ≥ 1 (weak accept) is a rough proxy for acceptance, 0.25–1 borderline.

## Reviewer form, field by field

### 1. Relevance To Conference

Type: free text.

Is this an information retrieval paper of interest to SIGIR? Explain briefly.

### 2. Relevance Score

Type: score, -1–2.

Fit to SIGIR.

- **2**: excellent
- **1**: good
- **0**: fair
- **-1**: poor

### 3. Novelty

Type: score, -1–2.

What is new beyond prior work; cite prior work for any "done before" claim.

- **2**: excellent
- **1**: good
- **0**: fair
- **-1**: poor

### 4. Technical Soundness

Type: score, -1–2.

Experimental design, baselines, statistical analysis, claims within the evidence.

- **2**: excellent
- **1**: good
- **0**: fair
- **-1**: poor

### 5. Quality Of Presentation

Type: score, -1–2.

Clarity of motivation, scope and results.

- **2**: excellent
- **1**: good
- **0**: fair
- **-1**: poor

### 6. Strengths

Type: list of points.

Specific strengths, substantiated with evidence from the paper.

### 7. Weaknesses

Type: list of points.

Specific weaknesses: unsuitable baselines, unreliable or non-generalizable results, evaluation mismatched to the research questions, missing statistical tests, insufficient detail to reproduce.

### 8. Additional Notes (optional)

Type: list of points.

Minor comments and suggestions.

### 9. Overall Recommendation

Type: free text.

Justify the overall score: the decisive strengths and weaknesses, weighing novelty, technical depth, elegance, impact and presentation.

### 10. Overall Recommendation Score

Type: score, -3–3.

Overall recommendation.

- **3**: strong accept
- **2**: accept
- **1**: weak accept
- **0**: borderline
- **-1**: weak reject
- **-2**: reject
- **-3**: strong reject

### 11. Nominate For Best Paper

Type: single choice.

Nominate only exceptional papers.

- 0 no
- 1 yes

### 12. Reviewer Confidence

Type: score, 1–5.

How certain you are about your assessment.

- **5**: I am absolutely certain that my evaluation is correct.
- **4**: I am confident, but not absolutely certain that my evaluation is correct.
- **3**: I am fairly confident that my evaluation is correct.
- **2**: I am fairly confident that my evaluation is correct, but I have significant doubts.
- **1**: My evaluation is an educated guess.

## Area chair meta-review

SIGIR senior PC members write the meta-review; PC chairs decide.

Recommendation options: Accept · Reject

## SIGIR checklist to strengthen an IR paper

1. Why the problem matters is clearly motivated
2. What is new beyond prior work is explicit
3. Claims stay within the evidence; scope (what was and was not done) is explicit
4. Research decisions are justified
5. Experimental design and scale fit the study; suitable baselines
6. Evaluation matches the research questions; proper statistical analysis
7. Enough detail, data and code to reproduce
8. Results presented clearly and discussed thoughtfully

## LLM policy

SIGIR follows the ACM Peer Review Policy and requires that referees not use AI systems to create their reviews; LLM-generated reviews are treated as highly irresponsible.

Official policy: https://sigir2026.org/en-AU/pages/submissions/submission-policies-and-information

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Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=sigir (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.