research-paper-writing

Drafting a publication-ready ML paper from a codebase requires structuring your story, verifying citations, and formatting for target conferences. This skill automates the full workflow from literature review to submission preparation using your experimental results.

Managing literature reviews for academic writing requires organizing related work and verifying every citation. This skill reduces the risk of fabricated references by grounding claims and bibliography entries in evidence during the drafting process.

Yes, you can format ML paper submissions for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. This skill guides conference-specific formatting, generates checklists, and structures reproducibility notes to ensure your academic writing meets target conference requirements.

Designing experiments for ML research requires logging results and tracking reproducibility to keep claims grounded. This skill plans experiment design and mandates experiment logging to ensure your figures, baselines, and claims remain fully supported by evidence.

You do not need final experimental results before starting academic writing, as this skill can draft from an idea or codebase. However, providing experimental results enables it to identify missing baselines and prepare a complete conference submission.

ML paper submissions get rejected for unsupported claims when experiment logging and citation verification are incomplete. This skill requires iterative refinement and reproducibility tracking to keep your claims, figures, and references grounded in evidence.