# ICML 2026 review form > Simulate ICML 2026 main-track reviews: Summary, Strengths and Weaknesses, Soundness / Presentation / Significance / Originality (1–4), Key Questions For Authors, the 1–6 Overall Recommendation and confidence. - Venue: International Conference on Machine Learning - Form status: official (from the venue's public reviewer form) - Official source: https://icml.cc/Conferences/2026/ReviewerInstructions - Overall score: Overall Recommendation, 1–6 - Rough acceptance bar (our simulation): mean ≥ 4 - Web page: https://reviewpaper.ai/venues/icml ## How reviewers read the paper ICML 2026 reviewers summarize the paper without critique, write a substantiated Strengths and Weaknesses assessment covering soundness, presentation, significance and originality (each also scored 1–4, with fair/poor ratings justified), ask 3–5 numbered key questions that say how each answer would change the score, check limitations, and give a 1–6 overall recommendation (3 and 4 sparingly) plus confidence. - Constructive, specific and substantiated. - Number the key questions and, for each, state how possible answers would move your score. - Every fair/poor sub-score must be justified in the weaknesses. **Where the bar sits.** ICML 2026 accepted about 26.6% of submissions. No public score statistics; inferred from NeurIPS's identical scale: mean ≥ 4 likely accept, 3.5–4 borderline. ## Reviewer form, field by field ### 1. Summary Type: free text. Briefly summarize the paper and its contributions in your own words. No critique, no pasted abstract. ### 2. Strengths Type: list of points. Part of "Strengths and Weaknesses". Cover soundness, presentation, significance and originality. Be specific and substantiate every point. ### 3. Weaknesses Type: list of points. Part of "Strengths and Weaknesses". Be specific and substantiate every point. Any Soundness/Presentation/Significance/Originality rating of fair or poor must be justified here. ### 4. Soundness Type: score, 1–4. Technical claims, methodology, and whether the central claims are supported with evidence. - **4**: excellent - **3**: good - **2**: fair - **1**: poor ### 5. Presentation Type: score, 1–4. Clarity, structure, and positioning relative to prior work. - **4**: excellent - **3**: good - **2**: fair - **1**: poor ### 6. Significance Type: score, 1–4. Importance of the problem and likely influence. - **4**: excellent - **3**: good - **2**: fair - **1**: poor ### 7. Originality Type: score, 1–4. New insights, methods, theory, data or perspectives; novel combinations and evaluations count. - **4**: excellent - **3**: good - **2**: fair - **1**: poor ### 8. Key Questions For Authors Type: list of points. Ideally 3–5 numbered questions whose answers would change your evaluation. For each, say how the possible answers would change your score. ### 9. Limitations Type: free text. Write "yes" if limitations and potential negative societal impact are adequately discussed; otherwise constructive suggestions. ### 10. Overall Recommendation Type: score, 1–6. Overall recommendation. 3 and 4 should be used sparingly. - **6**: Strong Accept: Technically flawless paper with exceptional impact on one or more areas of AI, with strong evaluation, reproducibility, and resources, and no unaddressed ethical considerations. - **5**: Accept: Technically solid paper, with high impact on at least one sub-area of AI or moderate-to-high impact on more than one area of AI, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations. - **4**: Weak accept: Technically solid paper that advances at least one sub-area of AI, with a contribution that others are likely to build on, but with some weaknesses that limit its impact (e.g., limited evaluation). Please use sparingly. - **3**: Weak reject: A paper with clear merits, but also some weaknesses, which overall outweigh the merits. Papers in this category require revisions before they can be meaningfully built upon by others. Please use sparingly. - **2**: Reject: For instance, a paper with technical flaws, weak evaluation, inadequate reproducibility, incompletely addressed ethical considerations, or writing so poor that it is not possible to understand its key claims. - **1**: Strong Reject: For instance, a paper with well-known results, unaddressed ethical considerations, or a poorly written paper where it is impossible to tell what the nature of its contribution is. ### 11. Confidence Type: score, 1–5. How certain you are about your assessment. - **5**: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully. - **4**: You 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. - **3**: You 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. - **2**: You 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. - **1**: Your assessment is an educated guess. The submission is not in your area or the submission was difficult to understand. Math/other details were not carefully checked. ### 12. Ethical Concerns Type: free text. "No" if none. Otherwise name the area (discrimination/bias/fairness; inappropriate applications and impact; responsible research practice; privacy and security; legal compliance; research integrity; other) and explain. ## Area chair meta-review ICML ACs decide from concrete arguments, not average scores, and use a fixed template when rejecting. Recommendation options: Accept (oral) · Accept (spotlight poster) · Accept (poster) · Reject ## ICML submission requirements 1. Impact Statement included (main track; does not count toward the page limit) 2. Main text within 8 pages at submission 3. Anonymized submission 4. Reproducibility details: hyperparameters, seeds, compute, and code availability 5. Limitations discussed ## LLM policy ICML 2026 assigns each reviewer Policy A (no LLMs) or Policy B (LLMs may help understand the paper and polish wording, but may not identify strengths/weaknesses or write the review). Violations can get the reviewer's own papers desk-rejected. Official policy: https://icml.cc/Conferences/2026/LLM-Policy --- Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=icml (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.