research-paper-writing

To map ML experiments to paper claims, use an experiment-to-claims pipeline that enforces evidence-based writing. This guides iterative research by requiring structured experiment journals and direct result validation for every narrative claim before drafting.

Citation verification in research paper writing is handled programmatically using dependencies like semanticscholar and habanero. This workflow cross-checks references against source databases and flags any unverifiable citations as placeholders to prevent hallucinated references.

LaTeX drafting and statistical analysis support major ML conference formats including NeurIPS, ICML, ICLR, and ACL. The workflow aligns drafts to venue-specific norms, generates figures using matplotlib, and refines the submission narrative after simulated review.

Designing iterative experimentation for AI research involves a non-linear loop where findings trigger new experiments and analysis. The process requires structured project organization, cost tracking, and maintaining an experiment log to connect results directly to prose.

This research workflow supports both empirical and some non-empirical ML paper types. It enforces disciplined claim-experiment mapping and structured project organization to ensure the submission narrative is fully supported by results across different paper categories.

Literature discovery requires programmatic verification to ensure all referenced works are authentic and accurately cited. Using arxiv and semanticscholar APIs, the workflow cross-checks references during discovery and flags unverifiable citations as placeholders to maintain submission integrity.