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 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 designed to help researchers determine the optimal conference or journal venue (e.g., AAMAS, NeurIPS, ICML, EC) for multi-agent AI research. It emphasizes that the contribution must center on the interaction result—such as mechanism design, coordination, or negotiation—rather than simply applying a single-agent method in a multiagent testbed. This tool sharpens the research framing before writing begins.

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 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.

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 detailing the empirical writing style required for EMNLP submissions. It emphasizes scoping claims to specific datasets and languages, ensuring examples are load-bearing, structuring the argument phenomenon-first, and accurately calibrating hedging language based on evidence strength.

This guide helps transform technically sound machine learning drafts into highly readable, conference-ready papers suitable for ICLR and OpenReview. It emphasizes structuring the core learning-representation insight upfront, clarifying contribution types (method, theory, benchmark), optimizing for public skimming, and providing clear reviewer navigation paths. Use this when ensuring maximum impact and clarity in a challenging academic review environment.

This comprehensive guide teaches researchers how to structure and refine machine learning papers for top-tier conferences like ICML. It focuses on achieving clarity, soundness, originality, and significance within strict page limits, providing actionable strategies to ensure the research withstands rigorous reviewer scrutiny.

A comprehensive guide for authors writing in computer vision, pattern recognition, or machine learning methods. This skill helps authors evaluate whether their manuscript meets the high methodological rigor, archival standards, and specific formatting requirements of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). It covers scope fit, evidence bar, structure, and pre-submission checklists.

A comprehensive guide for writing the 'Related Work' section for top-tier speech conference papers. It teaches authors how to trace the academic lineage of a task across multiple venues (e.g., ISCA, ICASSP, NeurIPS), properly cite canonical corpora and metrics, and condense complex literature reviews into concise, contrastive clauses.

A comprehensive guide for refining machine learning research papers tailored for NeurIPS submission. It teaches how to structure introductions, calibrate claims with evidence, explicitly state limitations, and address mandatory academic checklist items (e.g., reproducibility, broader impact). Use this skill to transform technical writing into a reviewer-proof, high-impact academic manuscript.