Skill UI

A comprehensive guide detailing the multi-phase review structure of the AAAI conference. It advises authors on navigating the two-phase review process, mitigating Phase 1 rejection risks, structuring effective rebuttals, and understanding decision mapping across pre-submission, discussion, and final decision stages.

This guide helps researchers strategically decide if their AI project is a strong fit for the AAAI conference. It provides detailed criteria, including strong and weak signals, and routing logic to determine whether the work should be submitted to AAAI, or routed to specialized venues like NeurIPS, ICML, or domain-specific conferences (e.g., CVPR, KDD). Use it to refine your project's framing and maximize submission success.

This guide provides a comprehensive project management framework for submitting research to the AAAI conference. It maps out every critical milestone, from initial topic selection and abstract submission, through the two-phase peer review, writing the rebuttal, and preparing the final camera-ready version and public artifacts. It emphasizes backward planning from hard deadlines and maintaining evidence integrity throughout the rigorous academic lifecycle.

This guide provides comprehensive instructions for packaging multiagent system (MARL) and game theory evidence for academic submissions, particularly for conferences like AAMAS. It emphasizes that the artifact must be inspectable, containing not just a trained model, but the game definition, environment code, opponent sets, and interaction protocols, ensuring rigorous reproducibility and reviewability.

This guide helps authors audit the novelty and eligibility of multiagent system research for submission to AAMAS. It provides comprehensive advice on positioning the work against literature spanning multiple top-tier venues (e.g., NeurIPS, ICML, AAAI, EC, JAAMAS), integrating concepts from multiagent RL, game theory, and social choice. It ensures submissions are positioned accurately compared to the broader academic community, maximizing impact and minimizing rejection risk.

This guide provides a detailed walkthrough of the AAMAS peer review process. It covers everything from initial submissions and technical evaluation (correctness, significance, rigor) to the rebuttal phase, area-chair synthesis, and final decisions. Use this when planning academic submissions or understanding the rigorous criteria for publishing multi-agent systems research.

This comprehensive guide provides a detailed audit checklist for authors submitting to the AAMAS conference. It covers verifying the correct conference track, maintaining double-blind anonymity, adhering to strict formatting rules (e.g., page limits, supplementary zip caps), and identifying potential submission-blocking risks to ensure readiness before the deadline.

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 for packaging and submitting evidence packages (artifacts) for NLP research papers, such as code, datasets, prompts, and model outputs. It details requirements for reproducibility, adhering to the Responsible NLP checklist, ensuring proper anonymization, and preparing for post-acceptance public release in academic settings like ACL.

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 revising NLP and Computational Linguistics manuscripts for submission to top-tier conferences like ACL. It mandates adopting a task-first, evidence-grounded approach, strictly scoping claims in the LLM era, and ensuring linguistic rigor in all examples and analyses. Learn how to structure, compress, and refine your arguments for maximum impact.