Skill UI

A comprehensive guide for authors preparing the single, short, double-blind rebuttal (Author Response) required during major AI conference review cycles (e.g., IJCAI-ECAI). It teaches authors how to precisely correct factual misreadings, supply missing evidence, and structure their arguments effectively within strict length limits, focusing only on verifiable facts rather than new claims or philosophical arguments.

A comprehensive guide detailing the strict requirements for submitting research papers to EMNLP via the ACL Rolling Review (ARR). Key areas covered include template compliance, page length restrictions (long/short format), mandatory limitations sections, double-blind anonymization techniques, and adherence to the Responsible NLP checklist. Use this audit guide before submission to ensure full compliance and avoid desk-rejects.

A comprehensive guide for structuring the "Related Work" section for top-tier computer vision conferences (ICCV, CVPR, ECCV). It covers best practices for citing intervening-cycle literature, managing concurrent preprints, ensuring double-blind anonymity, and correctly attributing source venues to maximize novelty perception and score highly with reviewers.

This comprehensive guide serves as a pre-flight audit checklist for major academic conference submissions, specifically targeting ICCV. It covers critical process areas, including the single-day upload requirement, strict page budget adherence, maintaining double-blind anonymity, vetting OpenReview fields, and identifying potential identity or formatting leaks. Use this to prevent desk rejection before the deadline.

Provides a comprehensive guide for planning strategy within major academic conference review systems, such as OpenReview. It details the double-blind review lifecycle, author response strategies, managing public vs. private discussions, and understanding how permanent visibility impacts decision-making. Ideal for authors and reviewers preparing for high-stakes academic submissions.

This guide provides a comprehensive audit checklist for submissions to major AI/ML conferences like ICLR. It covers critical areas including maintaining double-blind anonymity, adherence to page limits, required LLM disclosure, and mitigating desk-reject risks. Use this before submission deadlines to ensure your paper is fully compliant and ready for OpenReview.

A comprehensive guide for authors on drafting strategic, anonymous rebuttals and discussion replies for top-tier Machine Learning conferences like ICML. Learn how to triage reviewer objections by focusing on soundness, originality, significance, clarity, ethics, and reproducibility, adhering strictly to double-blind review constraints and maximizing impact in limited discussion rounds.

This skill serves as a comprehensive router for managing the entire lifecycle of an ICML manuscript. It guides users through every phase, including topic selection, drafting, artifact preparation, OpenReview submission, double-blind review, author response, and final camera-ready publication. It helps track risks, evidence, and official conference policies.

A comprehensive audit tool designed to ensure academic paper compliance before submitting to major AI conferences like NeurIPS. It systematically checks adherence to the CFP, double-blind anonymity across all assets (PDF, code, data, supplement), correct track selection, and avoids common desk-rejection pitfalls, ensuring the submission is ready for OpenReview.

A comprehensive guide detailing the highly structured, multi-stage peer review process used by top-tier robotics conferences (such as RSS). It covers the mechanics from double-blind submission and threshold gating to the role of the Area Chair. Learn how to structure your arguments, write compelling rebuttals, and summarize findings to maximize acceptance chances in highly selective academic venues.

A comprehensive guide for creating the two required artifacts for a WACV paper: an anonymous package for double-blind review, and a public release post-acceptance. It emphasizes reproducing deployed system claims (latency, power, robustness) and maintaining consistency across revision rounds.

A comprehensive guide for structuring the 'Related Work' section in academic papers, especially in Computer Vision (CV). It teaches authors how to strategically position their research contribution by covering multiple literature shelves: vision methods, domain constraints, concurrent arXiv work, and prior art. This ensures the paper clearly defines a unique gap rather than merely listing citations, adhering to double-blind review standards.