Skill UI

This comprehensive guide provides expert protocols for writing the 'Related Work' section in Machine Learning papers. It teaches authors how to maintain 'versioned honesty' by meticulously triaging the literature into distinct lanes (e.g., method lineage, model artifacts, evaluation infrastructure). It covers advanced topics like managing concurrent work, citing arXiv status accurately, and avoiding self-citation violations, ensuring the paper is positioned rigorously within the rapidly evolving academic field.

This guide details the rigorous process of preparing research artifacts for formal evaluation at major academic conferences like ICSE. It covers achieving ACM badges (Available, Reusable, Reproduced/Replicated) by ensuring proper DOI archiving, open licensing, robust documentation, and verifiable reproducibility for external reviewers, transforming raw research into publishable, trustworthy assets.

This guide clarifies what constitutes an 'artifact' for pure-theory venues like ITCS. It emphasizes that the core deliverable is not code or functional components, but the mathematical argument itself. Successful evaluation relies on providing complete, self-contained proofs within the submitted PDF, along with citing dependencies precisely. It details how to handle minor computational content to ensure static, verifiable checks.

A comprehensive guide detailing the entire academic review lifecycle for top-tier programming language conferences like PLDI. It covers every critical stage, from initial submission and double-blind review through author responses, final acceptance criteria, and post-publication artifact evaluation. Learn what Program Chairs look for, how to address soundness doubts, and how to optimize your paper for maximum impact.

This guide provides a rigorous methodology for achieving scientific reproducibility in technical and user experience papers (e.g., UIST). It details three essential 'ledgers': the Build Ledger (pinning software, hardware, and environment dependencies), the Measurement Ledger (defining precise protocols for metrics like latency and accuracy), and the Study Ledger (documenting human evaluation tasks). Following these protocols ensures that external researchers can accurately rebuild the artifact and validate reported results.