reviewpaper.ai
ModeledInternational Conference on Learning Representations

The ICLR 2026 review form, explained

Simulate ICLR 2026-style OpenReview reviews: Summary, Soundness / Presentation / Contribution (1–4), Strengths, Weaknesses, Questions, the new even-numbered 0–10 rating and confidence.

Modeled, not official. ICLR's guide is public, but the OpenReview form itself is not. Field order follows the long-standing ICLR form and the 2026 rating labels (0/2/4/6/8/10) come from secondary sources.

ICLR 2028 abstract deadline (estimated)~Sep 18, 2027 · 23:59 AoE~11 months
Overall score
Rating, 0–10
Sub-scores
Soundness, Presentation, Contribution (1–4)
Confidence
1–5
Form fields
10
Author checklist
5 items

How ICLR reviewers read your paper

ICLR's reviewer guide prescribes: summarize the claimed contribution; list strong and weak points; clearly state an initial recommendation with one or two key reasons; give supporting arguments; ask questions; optionally add feedback marked as not part of the decision. Reviewers should ask what problem is tackled, whether the approach is well motivated and placed in the literature, whether claims are supported correctly and rigorously, and whether the work is significant. Reviews are public on OpenReview.

  • Constructive tone — criticize with kindness; reviews are public.
  • Make the initial recommendation and its one or two key reasons explicit at the top of the weaknesses.
  • As comprehensive as possible; excessively short reviews get flagged.
  • State-of-the-art results are not required for a contribution.

Where the bar sits. ICLR 2026 accepted 27.4% of decided submissions. Paper Copilot means: posters ≈ 5.4, orals ≈ 6.4, rejected ≈ 4.0. Rough rule: mean ≥ 5 likely accept, ~4.4–5 borderline.

What do ICLR scores mean? Acceptance odds by score

The reviewer form, field by field

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

  1. 01

    Summary

    Free text

    Summarize what the paper claims to contribute. Be positive and constructive.

  2. 02

    Soundness

    Score · 1–4

    Are the claims adequately supported, and are the results technically correct and rigorous?

    1. 4excellent
    2. 3good
    3. 2fair
    4. 1poor
  3. 03

    Presentation

    Score · 1–4

    How clearly the paper communicates its ideas, methods and results.

    1. 4excellent
    2. 3good
    3. 2fair
    4. 1poor
  4. 04

    Contribution

    Score · 1–4

    Does it provide new, relevant, valuable knowledge to the ICLR community? State-of-the-art results are not required.

    1. 4excellent
    2. 3good
    3. 2fair
    4. 1poor
  5. 05

    Strengths

    List of points

    Strong points, as comprehensive as possible, each substantiated.

  6. 06

    Weaknesses

    List of points

    Weak points, as comprehensive as possible. Lead with the one or two key reasons behind your recommendation, then supporting arguments.

  7. 07

    Questions

    List of points

    Questions that would clarify your understanding or supply missing evidence. Additional feedback not part of the decision may be marked as such.

  8. 08

    Flag For Ethics Review

    Single choice

    Choose one.

    • No ethics review needed.
    • Yes, Discrimination / bias / fairness concerns
    • Yes, Privacy, security and safety
    • Yes, Legal compliance (e.g., GDPR, copyright, terms of use)
    • Yes, Potentially harmful insights, methodologies and applications
    • Yes, Responsible research practice (e.g., human subjects, data release)
    • Yes, Research integrity issues (e.g., plagiarism, dual submission)
    • Yes, Other reasons
  9. 09

    Rating

    Score · 0–10 (step 2)

    Initial recommendation. Clearly state accept or reject with one or two key reasons in the review text.

    ScoreMeaning
    10strong accept, should be highlighted at the conference
    8accept, good paper
    6marginally above the acceptance threshold
    4marginally below the acceptance threshold
    2reject, not good enough
    0strong reject
  10. 10

    Confidence

    Score · 1–5

    How certain you are about your assessment.

    ScoreMeaning
    5You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
    4You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
    3You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
    2You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
    1You are unable to assess this paper and have alerted the ACs to seek an opinion from different reviewers.

Area chair meta-review

ICLR meta-reviews are public. Base the decision on the arguments in the reviews, not the mean score.

  • Accept (Oral)
  • Accept (Poster)
  • Reject

ICLR author statements

Reviewers check these. Gaps are easy points to lose.

  1. Ethics statement (recommended, max 1 page, outside the page limit)
  2. Reproducibility statement pointing to code, proofs and experimental details (strongly encouraged)
  3. LLM usage disclosure if LLMs played a significant role in ideation or writing (required; non-disclosure can lead to desk rejection)
  4. Main text within 9 pages at submission (references and appendix unlimited)
  5. Anonymized submission (no identifying links or acknowledgements)

Policy on LLMs in reviewing

ICLR 2026 allows LLMs as general-purpose writing assistance, but reviewers are fully responsible for content and must disclose LLM use in the review form. Reviews were screened with LLM-text detectors.

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 ICLR 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 ICLR or its organizers. Forms change between cycles; always check the official source.