# COLT 2026 review form > Simulate COLT 2026-style learning-theory reviews: summary of results, significance and novelty relative to known bounds, correctness of the proofs, clarity, comments, an overall score and confidence. - Venue: Conference on Learning Theory - Form status: modeled, not official. COLT reviews in a private CMT form and publishes no reviewer guidelines. Every field and scale here is modeled on standard learning-theory reviewing (contribution, correctness, significance, clarity) and the CFP's reviewing philosophy. - Official source: https://learningtheory.org/colt2026/cfp.html - Overall score: Overall Score, 1–5 - Rough acceptance bar (our simulation): mean ≥ 3.5 - Web page: https://reviewpaper.ai/venues/colt ## How reviewers read the paper COLT asks reviewers to judge whether the work is close to being ready for publication and to help guide it there with constructive feedback. Papers are evaluated primarily on the main text, though all proofs must be included; there is an author response and discussion phase. Theory papers are judged on the significance and novelty of their results and techniques and on technical correctness, not on empirical evaluation. - Constructive: aim to help the paper reach publication quality. - Compare results precisely to the best known bounds and cite the prior work. - Say exactly which proofs were checked; point to specific steps for any error. - Do not ask for experiments unless the paper's claims are empirical. **Where the bar sits.** COLT 2026 accepted about 190 papers; submission counts are not published (historically ~30% acceptance). On this modeled 1–5 scale a mean of ≥ 3.5 is a rough proxy for acceptance. ## Reviewer form, field by field ### 1. Summary Of Results Type: free text. State the main theorems informally: setting, assumptions, the bounds or characterizations proved, and how they compare to the best known results. ### 2. Significance And Novelty Type: list of points. What is new relative to prior bounds and techniques (cite them), whether the techniques are of independent interest, and how the results advance learning theory. ### 3. Technical Correctness Type: list of points. Which proofs were checked and how carefully; any gaps, errors or unjustified steps (with locations); whether assumptions are reasonable and stated. Judgment rests primarily on the main text, but proofs must be complete in the appendix. ### 4. Clarity And Presentation Type: free text. Are the setting, results and proof ideas explained clearly in the main text? ### 5. Comments And Questions For The Authors Type: list of points. Constructive suggestions and questions for the author response, aimed at bringing the paper closer to publication. ### 6. Overall Score Type: score, 1–5. Overall recommendation. - **5**: Strong accept - **4**: Accept - **3**: Borderline - **2**: Reject - **1**: Strong reject ### 7. Confidence Type: score, 1–5. How certain you are, including how carefully the proofs were checked. - **5**: Expert: checked the proofs carefully and know the related literature well - **4**: Confident: checked the key proofs - **3**: Fairly confident: read the proofs at a high level - **2**: Somewhat confident: did not check the proofs - **1**: Educated guess ## Area chair meta-review COLT area chairs lead discussion and recommend; the form is not public. Recommendation options: Accept · Reject ## COLT submission requirements 1. At most 12 PMLR-format pages, plus unlimited references and appendices 2. All proofs included (main text should convey the key ideas) 3. Anonymized (double-blind) ## LLM policy COLT 2026 reviewers must not share submissions with any LLM; models may be used like books or Wikipedia to understand technical points, without leaking confidential information. Official policy: https://learningtheory.org/colt2026/cfp.html --- Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=colt (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.