Skill UI

A comprehensive pre-submission audit guide for authors submitting papers to EACL (Computational Linguistics). It covers critical areas such as adherence to format gates (8/4 content pages, required Limitations section), strict anonymity requirements, mandatory declarations (Responsible NLP, preprint status), and crucial advice regarding the single, non-fallback submission cycle. Use this before uploading to the ACL Rolling Review cycle.

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 for designing and auditing high-stakes computer vision experiments for top-tier conferences. It covers critical areas like 'matched-substrate fairness,' 'six-month-staleness testing,' and rigorous ablation studies, ensuring that claims are robust, reproducible, and withstand intense peer review scrutiny.

This guide outlines the complete project management workflow for major computer vision conferences like ECCV, CVPR, and ICCV. It covers the entire academic cycle, from initial registration and submission through peer review, acceptance, camera-readiness, and post-conference retargeting strategies. Useful for research teams planning multiple submissions across even and odd years.

A comprehensive guide for authors submitting their final, camera-ready version of an accepted EMNLP paper. It details the critical, multi-stage process, including merging reviewed edits, restoring author identity and public links, verifying Anthology metadata, and publishing associated artifacts. This ensures the manuscript meets all strict academic publishing and technical standards.

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.

This guide outlines best practices for structuring advanced NLP/AI research papers for academic submission. It details the critical differences between core content pages, mandatory limitations sections, discretionary appendices, and supplementary uploads, ensuring that reviewers are properly guided and that the work remains reproducible and verifiable.

This comprehensive guide assists authors in strategically positioning their computer vision manuscripts for the European Conference on Computer Vision (ECCV). It covers conference fit assessment, manuscript re-framing, required evidence (e.g., ablations, baselines, failure modes), and structural advice to ensure the work aligns with ECCV's scope and current review policies. Use this tool for venue selection and submission preparation.

This guide outlines the rigorous methodological standards for empirical work in AI fairness, accountability, and transparency (FAccT). It advises researchers on matching specific claims (e.g., disparate harm) to appropriate evidence types—including disaggregated metrics, coded qualitative data, and documented ethical procedures. It stresses the importance of ethical review, handling protected attributes, and acknowledging limitations to ensure high scientific and ethical rigor.

A comprehensive project management guide for submitting interdisciplinary, responsible AI papers to major academic conferences like FAccT. It provides a detailed backward-planning timeline, covering milestones from initial abstract registration and literature review to revisions, camera-ready submission, and conference presentation, emphasizing ethical considerations and structured academic processes.

This guide details the best practices for packaging, documenting, and releasing research artifacts (code, models, data) for top-tier ML conferences. It covers two stages: preparing a non-identifiable archive for anonymous review, and managing the public release runway, focusing on reproducibility, licensing, and sustained maintenance.

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.