Skill UI

A comprehensive audit guide for submitting papers to ICASSP conferences. This checklist covers crucial requirements such as the mandatory 4+1 page format, single-blind review rules (including author list inclusion), CMS portal submission protocols, EDICS metadata selection, and compliance with ethical standards. Use this guide before the deadline to avoid desk rejects.

A methodology guide for structuring high-stakes academic research experiments, particularly for top-tier CV conferences (like ICCV). It emphasizes systematic planning under strict deadlines, covering benchmark drift audits, ensuring fairness by accounting for pretraining confounds, structuring ablations to isolate mechanisms, and strategically sequencing runs to maximize impact before the submission deadline.

This comprehensive guide serves as a pre-flight audit checklist for major academic conference submissions, specifically targeting ICCV. It covers critical process areas, including the single-day upload requirement, strict page budget adherence, maintaining double-blind anonymity, vetting OpenReview fields, and identifying potential identity or formatting leaks. Use this to prevent desk rejection before the deadline.

A comprehensive guide for positioning database research papers for top-tier conferences like ICDE. It teaches authors how to distinguish genuine novelty (new mechanisms) from engineering delta (implementation improvements), conduct deep literature audits, and compare their work against both academic venues and real-world production systems. Essential for improving manuscript eligibility and impact.

A comprehensive audit checklist designed for authors submitting data-engineering systems papers to major conferences like ICDE. It covers critical areas including IEEE formatting standards, round selection strategy, Microsoft CMT conflict declarations, and the requirement for supplemental material, ensuring authors mitigate common desk-reject risks.

A comprehensive guide for researchers designing and auditing empirical evaluations for data mining papers (e.g., ICDM). It teaches how to structure a trustworthy evaluation by defining operational tasks, conducting ablation studies to isolate mechanisms, proving scalability, and implementing known-truth checks to ensure findings are valid and not mere artifacts.

A comprehensive audit guide for authors preparing submissions for the International Conference on Database Theory (ICDT). This guide covers critical aspects including selecting the correct submission cycle, adhering to the two-step abstract-then-paper deadline, meeting the LIPIcs formatting standards (15 pages excluding references), ensuring complete and verifiable proofs, and maintaining strict anonymity since 2024. Use this before uploading to Microsoft CMT.

This guide provides comprehensive best practices for designing and auditing empirical experiments for top-tier ML conferences. It covers necessary ablations, robustness checks (e.g., data shift, seeds), fair baselines, and detailed compute reporting to ensure that reported gains are scientifically genuine, reproducible, and address common reviewer skepticism.

This guide provides a comprehensive audit checklist for submissions to major AI/ML conferences like ICLR. It covers critical areas including maintaining double-blind anonymity, adherence to page limits, required LLM disclosure, and mitigating desk-reject risks. Use this before submission deadlines to ensure your paper is fully compliant and ready for OpenReview.

A comprehensive self-auditing guide designed for authors preparing manuscripts for submission to top-tier academic journals. It systematically addresses recurring, structural, and critical flaws across theory contribution, econometric identification, mechanism explanation, heterogeneity analysis, and writing conventions. This tool helps authors proactively identify and correct potential weaknesses before facing peer review.

This skill manages the end-to-end project lifecycle for academic conference submissions, specifically tailored for major venues like ICLR. It provides a structured timeline, defining critical milestones from initial topic selection and abstract submission through paper deadlines, public discussion, and final camera-ready versions. Use it to sequence research milestones, manage evidence paths, and ensure compliance with conference rules like anonymity audits and strict deadlines.

This guide provides a comprehensive framework for stress-testing Machine Learning experimental evidence before submission or rebuttal. It covers critical areas such as establishing strong baselines, conducting mechanism-isolating ablations, reporting variance, checking for data leakage, and disclosing compute costs. Use this to ensure your ML claims are scientifically sound, robust, and fair against rigorous academic scrutiny.