Skill UI

This guide provides comprehensive strategies for writing the Related Work section for submissions to AAAI. It teaches authors how to robustly position their paper's novelty by distinguishing contributions from both established archival literature and contemporaneous non-archival work (like arXiv papers). It also addresses specific reviewer expectations across various AI subfields (NeurIPS, ICML, ICLR) and outlines clear structural templates for maximum impact.

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.

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 provides a comprehensive pre-submission audit checklist for AAAI conference technical papers. It covers crucial aspects like double-blind anonymity, page limits, reproducibility requirements, author policies, and adherence to the latest AI-use guidelines. Use this before submitting to mitigate risks of desk rejection or policy violation.

This guide provides detailed rules for structuring supplementary material (technical appendices, code/data ZIPs, multimedia) for AAAI submissions. It emphasizes that core contributions and essential evidence must reside in the main paper, while appendices should only deepen the argument. It covers guidelines for maintaining double-blind anonymity, ensuring immutability, and achieving reproducibility.

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.

A comprehensive guide for structuring and writing high-quality AI research manuscripts intended for major academic conferences (e.g., AAAI). It teaches how to structure the first page for maximum impact, clearly state contributions (method, theory, system, etc.), ensure two-column readability, and address ethical limitations to appeal to a broad, non-specialist committee audience.

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.

A comprehensive guide for authors preparing rebuttals following AAMAS preliminary reviews. It details the specific constraints (e.g., double-blind, no new results), the strategies for addressing diverse technical reviewers (such as game theorists, MARL experts, and systems engineers), and the structured pattern required to maximize the impact of a short response window.

A comprehensive guide for authors transitioning an accepted, anonymous conference submission into the final, public camera-ready version for publication. It covers de-anonymization steps, including restoring author details, updating proceedings metadata, reflowing figures for multiagent papers, and integrating rebuttal promises without expanding the core contribution. This ensures adherence to strict publishing standards.

Provides comprehensive guidelines for designing, auditing, and reporting high-rigor multi-agent experiments, especially in game theory and self-play systems. It emphasizes testing the *interaction* claim—such as convergence to equilibrium, truthful behavior, or emergent cooperation—against held-out opponents, populations, and strategic deviations, moving beyond simple single-agent leaderboard scores.