Skill UI

A detailed guide for modeling the complete academic peer-review lifecycle, specifically tailored to the ECCV conference structure. It covers the timeline from submission through rebuttal and AC discussion to final decision, detailing critical points such as co-author enforcement regimes, LLM policy adherence, panel reviewer composition, and strategic points influencing the final outcome. Essential for researchers targeting top-tier vision conferences.

This guide provides a rigorous framework for designing and auditing empirical studies in software engineering (SE). It emphasizes that evidence must proportionally match the claims made, covering topics like using real subject systems, establishing fair baselines, calculating SE-standard statistics (effect sizes, CIs), and ensuring thorough provenance, especially for LLM-based work. It helps researchers strengthen their research methodology before publication.

A comprehensive guide modeling the ICCV review pipeline, covering the entire cycle from registration to final decision (March-June). It details author responsibilities, the enforcement of reviewer accountability, the prohibition of LLM use in reviews, and strategies for authors to interpret and leverage diverse review feedback, including utilizing the 'Findings' workshop.

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 guide for drafting and revising academic papers, particularly in the field of Large Language Models (LLMs). It teaches how to adopt the measurement-forward, evidence-based register required by top-tier conferences like COLM. Focus areas include leading with findings, strictly scoping claims to tested models and versions, structuring content for tight page limits, and maintaining a measured, critical voice.

A comprehensive audit checklist for ensuring final submissions to the ICML conference. It verifies compliance across critical areas including LaTeX formatting, strict page limits, double-blind anonymity, impact statements, dual submission rules, and adherence to the latest LLM and ethical policies. Essential for researchers preparing papers for top-tier AI venues.

A comprehensive guide detailing the academic standards for ensuring research reproducibility at ICSE. Covers open-science policies, mandatory data availability statements, techniques for pinning provenance (especially for mining and LLMs), and best practices for maintaining verifiability while ensuring anonymity. Essential for academic submissions.

A comprehensive methodology guide for designing and auditing empirical studies in software engineering, specifically focused on maintenance, evolution, and change impact. It outlines best practices for ensuring evidence is proportional to the claim, managing data provenance (SHAs, criteria), handling statistical confounds (survivorship, effect sizes), and integrating rigorous LLM evaluation protocols.

A comprehensive guide for researchers submitting academic papers to ensure high standards of reproducibility and verifiable evidence. It covers critical areas such as data-availability statements, artifact management, provenance pinning (for LLMs and mining), and mapping claims directly to evidence, adhering to rigorous academic standards.

A comprehensive checklist for auditing and preparing main-track conference submissions for top-tier AI venues like IJCAI. This guide covers critical compliance points, including double-blind anonymity enforcement, strict page limits, LLM usage policies, author details verification, and identifying potential desk-reject risks before final submission.

A comprehensive guide for researchers targeting the International Natural Language Generation Conference (INLG). This tool helps authors refine their manuscript by providing specific architectural advice on framing the contribution, establishing necessary evidence (e.g., rigorous evaluation, controllability metrics), and positioning the work correctly against related fields like dialogue and LLM generation. It ensures the paper meets the high standards expected by top NLP venues.

A comprehensive guide for designing and presenting rigorous experiments for top-tier computer science journals. It covers best practices for ensuring reproducibility, matching evidence to claims, selecting appropriate baselines, performing statistical significance testing, and handling modern challenges like LLM contamination and system scalability measurements. Essential for passing double-blind peer review.