Skill UI

This comprehensive guide helps researchers strengthen the reproducibility of AI academic papers for top-tier conferences like AAAI. It outlines best practices for creating a claim-to-evidence ledger, documenting seeds, hyperparameters, compute costs, and dataset licensing. Use this to ensure your work meets the highest standards of academic rigor and verifiability.

Provides comprehensive guidelines for authors preparing supplementary materials for conference submissions (AAMAS). It covers structuring full proofs, extended game definitions, extra experiments, and anonymized code assets, ensuring the main paper remains comprehensible while maximizing the impact of supporting evidence.

A comprehensive guide to modeling the entire academic review cycle for major NLP conferences like ACL. It details the stages from initial submission (ARR cycle intake) through peer review, author response, meta-review synthesis, commitment decision, and final venue decision (Main Conference, Findings, or Reject). It also outlines the roles of reviewers, area chairs, and program chairs, providing strategic advice for authors on navigating the complex publication landscape.

A comprehensive guide for authors preparing submissions for ACL conferences. It details how to effectively split content across the main body, required Limitations section, optional Ethics statement, and supplementary archives. Learn best practices for ensuring critical findings remain visible to reviewers, maximizing reproducibility, and structuring appendices for maximum impact.

A comprehensive guide for NLP researchers on strategically positioning their work before writing. It teaches how to determine the core linguistic contribution, select the optimal academic venue (ACL, EMNLP, etc.), manage the scope (long vs. short papers), and align the research focus with specific conference themes and review expectations, maximizing publication impact.

A specialized guide for authors targeting the ACM Conference on Fairness, Accountability, and Transparency (FAccT). It helps authors strategically frame their responsible AI research, ensuring claims are rigorously grounded in both technical measurement and social context. Covers necessary evidence bars, scope calibration, and submission best practices for this niche venue.

A comprehensive guide for authors targeting the ACM Conference on Intelligent User Interfaces (IUI). This skill helps evaluate manuscript fit, refine scope, understand evidence requirements, and strategically position papers related to AI-infused interaction, adaptive systems, and user modeling for top-tier HCI venues.

A comprehensive guide for revising technical papers submitted to ACM Multimedia (ACM MM). It teaches authors how to shift focus from single-modality benchmarks to cross-modal fusion mechanisms, ensuring all media elements (figures, audio, video) function as direct evidence. The guide emphasizes structuring the argument within a tight 6-8 page conference limit, making the multimedia contribution visible from page one.

A comprehensive guide for authors submitting papers to the ACL conference. It assists in positioning the manuscript, verifying topic fit (NLP, language models, semantics), and ensuring the evidence meets the high standards required for top-tier computational linguistics venues. Covers structure, best practices, and submission checklists.

A comprehensive guide for authors preparing manuscripts for the International Conference on Artificial Intelligence and Statistics (AISTATS). It advises on whether the paper's contribution is statistically grounded and theory-backed, guiding framing, evidence presentation, and understanding reviewer expectations in statistical machine learning.

A comprehensive guide for authors aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.

A comprehensive guide for ML research groups planning multi-venue conference submissions (e.g., COLM). It details the critical path of academic publishing, emphasizing backward induction from hard deadlines (abstract, paper, rebuttal, decision). The workflow covers compute budget allocation, co-author coordination, claim tracking, and managing the technical pipeline from initial topic selection to final presentation, ensuring reproducibility and timely execution in fast-paced AI research cycles.