Submission Instructions

Papers submitted to the workshop should be 4-8 pages long (not including references and appendix) and use the ICML formatting. Please change the footnote to reflect the name of the workshop: ICML 2026 Workshop on the Coevolution of Machine Learning and Combinatorial Optimization. Reviewing is double-blind. We invite the submission of ongoing unpublished work and work that is currently under review. The workshop is non-archival (there will be no proceedings), which allows for the papers to be submitted to other venues and should ensure that dual submission policies of other venues are not violated. Accepted papers will be made available on the workshop website. The program committee will select the top 5 outstanding papers to be highlighted as contributed talks at the workshop.

Submissions should be uploaded as a single PDF here: OpenReview

Camera-Ready Instructions

• The camera-ready submission is due June 1th, 2026.
• The camera-ready version can be up to 9 pages long (not including references and appendix).
• Just like for the submission, use the ICML formatting. Please change the footnote to reflect the name of the workshop: ICML 2026 Workshop on Machine Learning and Combinatorial Optimization.

Camera-ready versions should be uploaded as a single PDF in the submission portal: OpenReview

Reviewer Instructions

You can access your assigned papers through OpenReview. Reviews are due May 10th, AoE.

General information and review instructions:

• Please be constructive in your feedback to the authors. Note that preliminary work is also submitted to the workshop and we are generally leaning towards acceptance as long as the paper does not have glaring technical flaws or ethical concerns.
• Reviewing is double-blind.
• The submitted papers are between 4-8 pages long, which does not mean that an 8-page paper is necessarily better than a 4-page paper.
• There will be no rebuttal phase.
• Please pay special attention to how the paper fits within the theme of Machine Learning and Combinatorial Optimization.
• Consider the novelty, technical soundness, experimental validation, and potential impact on the ML&CO community.
• If your expertise does not match perfectly with an assigned submission, please still review the submission based on your experience and broad knowledge. The PCs will take a closer look at these instances before making decisions.

Thank you for your service in making the workshop a success!