Skill UI

Comprehensive guidelines for structuring and submitting reproducible AI/ML research papers for top-tier conferences. It details two modes: Mode 1 for theoretical/KR work (requiring full, explicit proofs in the supplement), and Mode 2 for empirical/ML work (requiring fixed seeds, cached outputs, and rigorous provenance pinning for datasets, models, and environments). The guide also covers best practices for maintaining double-blind anonymity and properly dividing content between the main body and the supplementary material.

This guide helps researchers determine the optimal academic conference (ECAI, IJCAI, AAAI, etc.) for their AI contribution *before* starting to write. It provides structured tests (like the Model-swap test and General-audience test) and criteria (contribution shape, main track vs. application) to ensure the work is optimally matched to a specialized venue, maximizing the chances of acceptance and impact.

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.

A comprehensive guide for authors submitting computer vision papers (e.g., to ECCV). It outlines best practices for ensuring the full reproducibility of AI research, covering everything from detailed training recipes and hyperparameter logging to pinning foundation model dependencies and reporting variance honesty. Essential for academic publication integrity.

This guide provides comprehensive instructions for preparing and submitting supplementary material for computer vision conferences like ECCV. It details how the supplement should support the main paper's claims without adding new results, covering topics such as qualitative result visualization, detailed ablation studies, code archive scrubbing, and ensuring full anonymity across all uploaded assets.

A comprehensive guide for computer vision researchers to determine if their project is a good fit for ECCV, considering scope, technical durability, and academic timing. It weighs the project's contribution against the specific requirements of ECCV versus competing top-tier venues like CVPR and ICCV, ensuring strategic submission planning.

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 for researchers submitting NLP papers, detailing best practices for achieving high reproducibility. It instructs users to pin model versions, log data provenance, report compute costs, and adhere rigorously to the Responsible NLP checklist. Essential for ensuring academic integrity and enabling rigorous verification of ML experiments.

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.

A comprehensive guide for researchers deciding where to submit an empirical NLP paper. It outlines EMNLP's focus on empirical methods, provides an 'empirical-identity test,' and compares submissions across major venues (ACL, ML, CoNLL, etc.) to help authors choose the most suitable program and pitch.