What problem does it solve? Writing a publication-ready ML/AI paper involves coordinating experiments, verified citations, statistical analysis, LaTeX formatting, and venue-specific checklists, and mistakes in any step cause desk rejections or hallucinated references. ## Core Features & Use Cases - Full Research Lifecycle: Covers project setup, literature review, experiment design, execution monitoring, statistical analysis, drafting, self-review, and submission for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. - Verified Citation Workflow: Fetches BibTeX programmatically via Semantic Scholar, CrossRef, and arXiv APIs instead of generating citations from memory, with a mandatory 5-step verification process. - Venue Templates & Checklists: Ships official LaTeX templates and mandatory checklist requirements (NeurIPS 16-item checklist, ACL Limitations section, ICLR LLM disclosure) to prevent desk rejection. - Use Case: A researcher with experimental results asks the agent to draft an ICML submission; the skill maps claims to experiments, verifies every citation, generates booktabs tables and colorblind-safe PDF figures, and runs a simulated reviewer pass before submission. ## Quick Start Help me write a NeurIPS paper from the experiment results in my results/ directory, starting with a one-sentence contribution statement.