What problem does it solve? Writing a publication-ready ML/AI research paper involves coordinating experiments, verified citations, statistical analysis, LaTeX formatting, and venue-specific checklists, and mistakes like hallucinated citations or missing NeurIPS checklist items cause desk rejections. ## Core Features & Use Cases - Full Research Lifecycle Pipeline: 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 for six conferences plus pre-submission checklists covering page limits, reproducibility statements, and ethics requirements. - Use Case: A researcher with experimental results in a codebase asks the agent to design ablation studies, run them with monitoring, analyze significance with McNemar's test, and produce an ICML-formatted draft with verified references. ## Quick Start Help me turn my experiment results in this repository into an ICML 2026 submission draft with verified citations and statistical analysis.