# Generic ML/AI 2026 review form > A generic ML/AI conference review format (not any venue's official form): Summary, Strengths, Weaknesses, Questions, Limitations, Soundness / Presentation / Contribution (1–4), a 1–10 overall score and 1–5 confidence. Use it for venues we do not have a template for. - Venue: Generic ML/AI conference - Form status: official (from the venue's public reviewer form) - Official source: https://reviewpaper.ai/venues/generic - Overall score: Overall, 1–10 - Rough acceptance bar (our simulation): mean ≥ 6 - Web page: https://reviewpaper.ai/venues/generic ## How reviewers read the paper A generic format that combines the fields most major ML/AI conferences share: a faithful summary, substantiated strengths and weaknesses across soundness, presentation, significance and originality, questions that could change the assessment, a limitations check, three 1–4 sub-scores, a 1–10 overall recommendation and a 1–5 confidence. It is not any venue's official form; for a specific venue, check its reviewer guidelines. - Constructive, specific and substantiated; every criticism names what, where and why. - Lead the weaknesses with the one or two issues that drive the recommendation. - Do not reject solely for not beating state of the art, or claim a lack of novelty without naming the prior work. - Every fair/poor sub-score must be justified by a weakness. **Where the bar sits.** Generic calibration, not a venue statistic: selective ML/AI conferences accept roughly 20–30% of submissions. On this 1–10 scale a mean of 6 or more is competitive, 5–6 is borderline, and below 5 is a likely reject. ## Reviewer form, field by field ### 1. Summary Type: free text. Summarize the problem, approach and main claimed contributions in your own words. No critique; the authors should agree with it. ### 2. Strengths Type: list of points. Reasons to accept: soundness, clarity, significance and originality, each substantiated with evidence from the paper. ### 3. Weaknesses Type: list of points. Reasons to reject, most important first. Be specific: say what is wrong or missing, where, why it matters for the claims, and what would fix it. Separate fatal flaws from fixable issues. ### 4. Questions Type: list of points. Questions and requests for the authors whose answers could change your assessment; say how. ### 5. Limitations Type: free text. Are limitations and potential negative societal impacts adequately discussed? Write "yes" if so; otherwise give constructive suggestions. Do not penalize candor. ### 6. Soundness Type: score, 1–4. Are the claims supported by correct and rigorous theory or experiments? - **4**: excellent - **3**: good - **2**: fair - **1**: poor ### 7. Presentation Type: score, 1–4. Clarity, organization, and positioning relative to prior work. - **4**: excellent - **3**: good - **2**: fair - **1**: poor ### 8. Contribution Type: score, 1–4. Significance and originality of the contribution to the field. - **4**: excellent - **3**: good - **2**: fair - **1**: poor ### 9. Overall Type: score, 1–10. Overall recommendation. Use the borderline scores (5, 6) when the case really is balanced. - **10**: Award quality: technically flawless, with groundbreaking impact. - **9**: Very strong accept: excellent paper, among the best at the venue. - **8**: Strong accept: technically strong, with high impact on its sub-area. - **7**: Accept: technically solid, with clear impact; minor weaknesses only. - **6**: Weak accept: reasons to accept outweigh reasons to reject. - **5**: Borderline: merits and weaknesses roughly balance. - **4**: Weak reject: reasons to reject outweigh reasons to accept. - **3**: Reject: significant flaws in soundness, evaluation or clarity. - **2**: Strong reject: major technical flaws or well-known results. - **1**: Trivial or wrong: fundamentally flawed or out of scope. ### 10. 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. ## Area chair meta-review Decide from the arguments in the reviews, not the mean score. Recommendation options: Accept (oral) · Accept (poster) · Reject ## LLM policy Policies differ by venue, and most forbid uploading submissions to external LLMs or using them to write reviews. Check the target venue's reviewer guidelines; this output is for authors' self-assessment only. Official policy: https://reviewpaper.ai/venues/generic --- Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=generic (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.