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 researchers deciding the optimal conference venue for their NLP project. It analyzes whether a project is best suited for EACL, ACL, EMNLP, NAACL, or other specialized venues (like LREC-COLING or COLM). The guide considers scope, contribution type (e.g., analysis vs. new system), temporal constraints, and the trade-offs between long and short paper formats.

A comprehensive project management skill designed for authors preparing papers for the EACL conference. It maps out the entire single-cycle submission process, covering backward planning from the ARR deadline, rebuttal strategies, commitment decisions, and final camera-ready stages. Essential for navigating highly structured academic publication timelines and minimizing risk.

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 academic researchers on how to structure and support claims in AI papers, particularly for top-tier conferences like ECAI. It details how to match the type of claim (e.g., completeness, efficiency, generalization) to the appropriate evidence—whether it requires a formal proof, a controlled empirical comparison, or a real-world deployment demonstration. The guide covers best practices for ML (seeds, ablations, baselines), multi-agent systems, and ensuring full provenance and academic honesty.

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.