Skill UI

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 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.

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.

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.

This guide provides a comprehensive strategy for authors submitting NLP and Computational Linguistics papers to EACL. It covers conference positioning, manuscript re-framing from journal or arXiv styles, and establishes rigorous evidence standards. It helps authors ensure their work aligns with the European ACL community focus, optimizing the paper's narrative structure for high acceptance rates.

A comprehensive guide to preparing accountability artifacts for AI research papers, particularly for venues like FAccT. It covers the necessary documentation genres—including datasheets for datasets, model cards for models, and impact assessments—to ensure transparency, verifiability, and accountability regarding claims of fairness and harm mitigation in AI systems. The focus is on making research outputs rigorously traceable and ethically responsible.

A comprehensive audit guide for submitting academic papers to the FAccT conference. It details critical, multi-step processes, including the mandatory abstract registration gate, maintaining mutual anonymity, adhering to strict ACM formatting rules, and including required endmatter statements (such as the Generative AI Usage Statement).

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.

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.