Skill UI

This guide outlines the standards for making pure theoretical claims independently verifiable. It demands complete, self-contained proofs for all central lemmas, explicit definitions for every symbol, and precise citation for all borrowed results. It ensures that the submitted paper, the full preprint version, and the mathematical arguments are perfectly consistent, addressing the highest level of academic rigor in theoretical computer science.

This guide details the crucial rules for structuring a scientific paper submitted to conferences like ITCS. It dictates how content—including main theorems, proofs, and motivation—must be distributed across three locations: the main body, the submitted appendix, and the external full version (arXiv). The core principle ensures that all decision-critical evidence for accepting the paper resides within the submitted PDF, preventing reviewers from relying on external or unread preprints.

This guide provides a comprehensive overview of the editorial and review process for the Journal of Public Economics (JPubE). It details the single anonymized review model, expectations regarding multiple specialist reviewers, the use of Editorial Manager, and strategic advice for SSRN preprints and appeals. Use this skill when planning a submission, understanding review outcomes, or managing academic publication expectations.

This comprehensive guide equips authors with a structured framework for writing the Related Work section for top-tier NLP conferences (e.g., NAACL). It emphasizes tracking methodological, data, and task lineages, ensuring citation precision across Findings, workshops, and preprints, and culminating in a single, falsifiable "delta sentence" to precisely position the novel contribution.

This skill guides authors on how to strategically position their machine learning research against complex academic landscapes. It covers comparing work to neighboring conferences (ICML, ICLR), analyzing contemporaneous preprints, and defining the exact technical delta from prior methods to minimize novelty risk for top-tier AI venues.

A comprehensive guide for maintaining consistency and completeness of references for PNAS Nexus submissions. It details the critical differences between the format-neutral requirements at submission time and the final numbered style required for publication, emphasizing the proper citation of datasets, software, and preprints.

This guide provides a comprehensive framework for positioning theoretical algorithms research in top-tier venues like SODA. It details how to conduct a rigorous "related work" review, covering bound lineages across conference proceedings, journal publications, and arXiv preprints. Learn best practices for citing dual-version results, managing priority claims, and writing scholarly prose to establish your result's state-of-the-art status.

A comprehensive guide for theoretical computer science authors on converting an accepted paper into two distinct, public deliverables: a concise, citable proceedings version (extended abstract) and a comprehensive, full version for public preprint servers like arXiv. Details the formatting, copyright requirements, version control, and post-publication hygiene necessary for academic publishing.

A comprehensive guide for authors writing theoretical computer science papers. It details how to position a result within the existing academic lineage, ensuring rigorous citation practices, managing version discrepancies (preprint vs. journal), and honestly comparing bounds against prior work. Essential for meeting the high standards of top-tier conferences like STOC.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.