Skill UI

This comprehensive checklist is designed for auditing NLP research papers, particularly for major conferences like EACL. It serves as a claims audit, ensuring that every reported result, claim, or finding—including hyperparameters, compute costs, verbatim prompts, data sources, and significance testing—is fully documented and reproducible. It emphasizes academic rigor and transparency in AI research.

This comprehensive guide helps researchers revise NLP research papers for top-tier conferences like EACL. It focuses on structuring the argument for fast legibility by emphasizing a clear Task-Gap-Method-Result-Scope arc, quantifying results with statistics, performing detailed error analysis, and maintaining academic rigor, anonymity, and multilingual clarity. Ideal for polishing conference submissions.

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.

This guide provides best practices for authors writing the Related Work section for top-tier AI conferences (e.g., ECAI). It instructs users on how to cover diverse AI lanes (symbolic, learning, multi-agent, application) for broad reviewers, employ 'Delta-first' positioning to state novelty concisely, and adhere to strict page budgets and double-blind review rules.

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 models the comprehensive review pipeline for ECAI submissions, detailing the single-round, double-blind process. It covers the summary-reject phase, full review by area chairs, the single rebuttal window, and the final accept/reject decision. It contrasts this process with journal revisions and other major AI conferences (AAAI, IJCAI) to help authors strategize for submission success.

A comprehensive guide for authors submitting to major AI conferences (like ECAI). It teaches the critical skill of separating core, decision-critical arguments (must be in the main body) from supporting details, proofs, and code (which belong in the optional supplement). This ensures reviewers can fairly judge the contribution despite strict page limits.

This comprehensive guide outlines a detailed, backward-planning workflow for submitting research papers to major AI conferences like ECAI or IJCAI-ECAI. It covers the entire academic lifecycle, from initial topic selection and evidence type planning to abstract submission, full paper drafting, rebuttal response, and final camera-ready publication, ensuring authors meet strict deadlines and adhere to rigorous review standards.

This guide provides essential structural advice for authors writing high-impact research papers for top-tier AI conferences. It covers the 'first-page arc,' ensuring the contribution is generalizable (not model-specific), matching evidence types (proofs vs. empirical data) to claims, and maintaining narrative density over a limited page count. Perfect for researchers preparing submissions.

A comprehensive guide on how to structure and write a high-impact academic rebuttal for top-tier conferences like ECCV. It advises authors on strategic space management, prioritizing the rebuttal of misreadings, conceding limitations gracefully, and preparing arguments specifically for the Area Chair (AC) discussion, ensuring the limited page count is used efficiently.

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.