Skill UI

Guides researchers through drafting publication-ready machine learning papers for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM by combining expert writing philosophy with LaTeX templates, citation verification APIs, and collaborative workflows.

Automatically generates structured academic papers in LaTeX format. By integrating a detailed paper plan, the research narrative, and generated figures, this tool structures the content section by section. It supports major conference and journal templates (e.g., ICLR, NeurIPS, IEEE), manages complex citation fetching via DBLP/CrossRef, and allows structural guidance using reference papers. Ideal for drafting the complete manuscript.

A sophisticated pipeline that generates professional, print-ready academic conference posters (e.g., for ICLR, NeurIPS). It builds the poster as a single HTML/CSS file and renders it precisely to PDF using headless Chromium. The system enforces measurement-driven hard gates, utilizes a two-hue design system, and ensures figure provenance, guaranteeing structural integrity for publication standards.

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 helps researchers assess the optimal venue for their work, determining if their project aligns best with AISTATS, or if it is better suited for major ML conferences (NeurIPS, ICML, ICLR), UAI, or specific statistics journals. It emphasizes the need for strong statistical foundations, inference guarantees, and the unique intersection of AI, ML, and statistics before starting the writing process.

This skill helps authors strategically decide the optimal venue for their Language Model research. It provides the critical "object-of-study test" to determine if the LM itself is the subject of the study, or merely a tool for a task. Furthermore, it guides routing against top-tier NLP/AI conferences (ACL, NeurIPS, ICLR, etc.), helping authors target the right audience and maximizing submission chances.

This guide details the process of packaging artifacts for academic conferences (ICLR), ensuring that research claims are fully reproducible and reviewable. It emphasizes providing minimal reproduction paths, maintaining data provenance, rigorously anonymizing metadata to protect authors' identities, and creating robust, permanent records for the scholarly community.

A comprehensive guide for authors on drafting strategic responses to conference reviews (e.g., ICLR OpenReview). It covers differentiating public vs. restricted comments, structuring rebuttals, addressing specific concerns (novelty, reproducibility), and maintaining professional communication standards for permanent records.

This comprehensive guide outlines the critical steps for authors preparing their accepted ICLR paper for camera-ready submission. It covers de-anonymization procedures, incorporating all fixes promised during the discussion period, completing OpenReview metadata, and assembling all necessary presentation assets, including posters, slide decks, and videos, according to current conference guidelines.

This guide provides comprehensive best practices for designing and auditing empirical experiments for top-tier ML conferences. It covers necessary ablations, robustness checks (e.g., data shift, seeds), fair baselines, and detailed compute reporting to ensure that reported gains are scientifically genuine, reproducible, and address common reviewer skepticism.

This guide provides a comprehensive framework for authors to position their work against existing literature, concurrent submissions, and public scrutiny in top-tier AI conferences. It teaches how to separate similar components from identical scientific problems, build verifiable novelty claims, and respond robustly to reviewer challenges.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.