academic-review

An automated academic review tool detects the research domain of your OR and ML+OR papers, applies domain-specific checklists, and performs adversarial reviews to provide structured feedback for top-tier journals.

Reviewing ML+OR submissions for top-tier journals is best handled by a closed-loop workflow that applies targeted domain checklists and uses adversarial review agents to ensure robust, precise review recommendations.

Yes, automated academic review uses domain detection to automatically identify the research domain of your paper and applies targeted checklists based on domain-specific criteria for precise evaluation.

Adversarial review for Operations Research papers works by using two agents to independently analyze the submission, ensuring robust conclusions and actionable recommendations through a closed-loop workflow.

Yes, expertise in Operations Research, Machine Learning, and academic publishing standards is required to handle input papers and provide structured, domain-specific feedback for top-tier journals.

You can invoke the academic review by using the slash command `/academic-review <path-to-results>` with your target paper, automating the closed-loop review workflow and generating structured feedback.