Skill UI

This guide details the necessary workflow for organizing supplementary academic content, such as code, extensive methodologies, and raw data, across various conference submission phases. It emphasizes placing only core arguments in the main body, keeping sensitive materials anonymized in the mirror, and preparing a staging shelf for efficient revision.

A comprehensive guide for structuring Human-Robot Interaction (HRI) conference submissions. It outlines the critical distinction between core, decision-making content (which must remain in the 8-page body) and supplementary materials (e.g., extended tables, instruments, codebooks). Special emphasis is placed on treating the video figure as a primary artifact, ensuring it is short, legible, and properly anonymized for double-blind review.

A critical guide for authors submitting to ICALP (International Conference on Logic, Artificial Intelligence and Programming). It clarifies that ICALP is a pure theoretical venue where the core contribution is a mathematical theorem and its verifiable proof, not a functional code artifact. This document advises authors from systems or ML backgrounds on how to adjust their focus and what constitutes a valid contribution.

This guide details the best practices for packaging, documenting, and releasing research artifacts (code, models, data) for top-tier ML conferences. It covers two stages: preparing a non-identifiable archive for anonymous review, and managing the public release runway, focusing on reproducibility, licensing, and sustained maintenance.

A comprehensive guide for authors submitting to top-tier computer vision conferences (like ICCV). It details the new single-deadline workflow, emphasizing the parallel production of code and videos. Learn how to structure supplementary materials for maximum reviewer impact, including detailed ablation studies, failure mode galleries, and maintaining strict double-blind anonymity in the archive.

Provides comprehensive guidelines for packaging research artifacts (code, data, logs) for submission to data mining conferences like ICDM. The focus is on ensuring the artifact is completely anonymous for triple-blind review (scrubbing Git history, paths, and internal names) and maximizing reproducibility, allowing reviewers to verify results with minimal effort.

This guide outlines the specific expectations for preparing the 'artifact' when submitting papers to the International Conference on Database Theory (ICDT). It clarifies that ICDT is a pure theory venue, meaning the primary deliverable is not runnable code or system badges, but the complete, verifiable proof. Key components include the marked appendix (for review) and the consistent, archived full version on arXiv.

This guide outlines best practices for structuring academic papers submitted via OpenReview, particularly for venues like ICLR. It advises authors on where to place different types of evidence—such as core claims, full derivations, code, and large datasets—to ensure the central argument remains visible while maintaining academic rigor. It covers strategies for managing annexes, maintaining anonymity, and updating revisions.

Provides comprehensive guidelines for packaging supplementary materials, including code, data, model weights, and benchmarks, for major machine learning conferences like ICML. It details the requirements for both the double-blind review stage (focusing on anonymization and minimal replication commands) and the final public release package (ensuring permanent public links and clear licensing).

A comprehensive guide for researchers detailing how to strengthen the reproducibility of AI/ML papers for top-tier conferences like ICML. It provides an evidence checklist covering data sources, anonymized code, random seeds, compute budget disclosure, and theoretical assumptions, ensuring claims are robust and verifiable.

This guide outlines best practices for packaging research artifacts (code, data, proofs, models) submitted for major AI conferences like IJCAI. It helps researchers create convincing, anonymous, and reproducible evidence to support their claims, even when formal artifact evaluation tracks are unavailable.

A comprehensive checklist and guide for authors preparing their accepted research papers for final publication (camera-ready). This process involves de-anonymization, restoring full author details, updating copyright and metadata, ensuring formal formatting, and managing the release of promised artifacts and code for reproducibility.