Why Markdown for LLMs?

Markdown can act as an interface between human authors, language models, and the software handling their outputs. This paper examines headings, lists, code blocks, and document hierarchy as tools for readable, reusable LLM workflows.

It compares that role with plain text, JSON, and XML, discusses Markdown-awareness evaluation, and identifies cases that need stronger machine validation. Its proposed tests separate workflow efficiency from model-output quality.