# TMLR 2026 review form > Simulate TMLR reviews: Summary of contributions, the two acceptance questions (are the claims supported by evidence? would some of TMLR's audience care?), requested changes marked critical or optional, broader-impact concerns, and the Accept / Leaning Accept / Leaning Reject / Reject recommendation. - Venue: Transactions on Machine Learning Research - Form status: official (from the venue's public reviewer form) - Official source: https://jmlr.org/tmlr/reviewer-guide.html - Overall score: Recommendation, 1–4 - Rough acceptance bar (our simulation): mean ≥ 3 - Web page: https://reviewpaper.ai/venues/tmlr ## How reviewers read the paper TMLR is a journal with rolling submissions. Acceptance rests on two questions only: are the claims supported by accurate, convincing and clear evidence, and would at least some of TMLR's audience be interested in the findings? TMLR explicitly avoids judging novelty, significance or impact: a narrow but well-supported contribution should be accepted. Presentation problems matter only if they materially get in the reader's way. Reviewers list requested changes, each marked critical or merely strengthening, flag broader-impact concerns, and after discussion recommend Accept / Leaning Accept / Leaning Reject / Reject (plus optional certifications). Reviews are public. - Do not reject for lack of novelty, significance or state-of-the-art results; judge only whether claims are supported and whether some audience would care. - If over-claiming is the problem, say which claims to scope down rather than rejecting the work outright. - Mark each requested change as critical to acceptance or as merely strengthening the paper. - Respectful, engaged tone; reviews become public on OpenReview. **Where the bar sits.** TMLR has no acceptance quota and publishes no rate. The action editor accepts when both criteria hold (claims supported, some audience interested), so a mean of 3 (Leaning Accept) or more is the rough line. Recommendation scores are mapped 1 Reject … 4 Accept by us; the labels are TMLR's. ## Reviewer form, field by field ### 1. Summary Of Contributions Type: free text. The paper's contributions in your own words, with its key strengths and weaknesses in brief. Not a substitute for the two criteria answers below. ### 2. Are the claims made in the submission supported by accurate, convincing and clear evidence? Type: single choice. The most important criterion. Judge only whether the stated claims are backed by accurate, convincing and clear evidence. Novelty and significance are explicitly NOT criteria; narrow claims that are fully supported are fine. - Yes - No ### 3. Claims And Evidence: Explanation Type: list of points. Explain the answer above: for each central claim, whether and how the evidence supports it. Unsupported or over-stated claims are the main reason to reject; say how they could be scoped down or supported. ### 4. Would at least some individuals in TMLR's audience be interested in knowing the findings of this paper? Type: single choice. A low bar: if unsure whether anyone would be interested, assume they would. Answer No only for findings no ML researcher or practitioner would care about. - Yes - No ### 5. Audience: Explanation Type: free text. Who in TMLR's audience would find the findings useful, and why (or why no one would). ### 6. Requested Changes Type: list of points. Proposed adjustments to the submission. Start each with "[Critical]" if it is critical to securing your recommendation for acceptance, or "[Strengthening]" if it would simply strengthen the work. ### 7. Broader Impact Concerns Type: free text. Ethical concerns that would require adding a Broader Impact Statement, or that the existing statement does not adequately address. "None" if there are none. ### 8. Recommendation Type: score, 1–4. Submitted on the separate Official Recommendation form after discussion. Must be consistent with the two criteria: Accept or Leaning Accept if and only if both answers are Yes. - **4**: Accept - **3**: Leaning Accept - **2**: Leaning Reject - **1**: Reject ## Area chair meta-review TMLR action editors decide from the two criteria, not from novelty. The decision has three outcomes: Accept as is, Accept with minor revision, or Reject (optionally inviting a major-revision resubmission). Recommendation options: Accept as is · Accept with minor revision · Reject ## LLM policy TMLR lets reviewers use LLMs and other tools to assist, e.g. to look up notation or polish grammar, but reviewers must form their own understanding of the paper and take responsibility for their reviews. Largely LLM-generated reviews are closely examined. Official policy: https://jmlr.org/tmlr/reviewer-guide.html --- Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=tmlr (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.