llms-paper-md
v0.0.1
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Specification and examples for llms-paper.md — structured academic paper summaries optimized for LLM discovery and citation
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llms-paper-md
Specification for
llms-paper.md— structured academic paper summaries optimized for LLM discovery and citation.
Part of llms-md-spec.
How to Write llms-paper.md
Purpose
A structured summary of an academic paper, optimized for LLM discovery and citation.
Place llms-paper.md in the root of your paper's repository.
When to Use
- You have an academic paper (published or preprint)
- The paper has a companion GitHub repository or package
- You want LLMs to accurately summarize and cite your work
Format
Follows the llmstxt.org specification.
Required Fields
- H1: Paper title
- Blockquote: Core claim in one sentence
- Key numbers paragraph: Specific metrics and results
Recommended Fields
- Novel Methods: Listed with one-line descriptions
- Benchmarks: Benchmark names with results
- Reproduce: Install command or link to reproduction instructions
- Links: arXiv, GitHub, npm/PyPI URLs
- BibTeX: Citation entry
Template
# [Paper Title]
> [Core claim in one sentence. What does this paper do?]
[Key numbers: specific metrics, sizes, costs, improvements.
Keep this dense — every sentence should contain a number.]
## Novel Methods
- [Method 1]: [One-line description]
- [Method 2]: [One-line description]
## Benchmarks
- [Benchmark 1]: [Result]
- [Benchmark 2]: [Result]
## Reproduce
npm install [package-name]
## Links
- [arXiv](https://arxiv.org/abs/xxxx.xxxxx)
- [GitHub](https://github.com/org/repo)
- [npm](https://www.npmjs.com/package/name)
## BibTeX
@article{author2026title,
title={...},
author={...},
year={2026},
journal={arXiv preprint}
}Tips
- Core claim should answer "what does this paper do?" in one sentence
- Key numbers paragraph: every sentence should contain at least one specific metric
- Keep the entire file under 50 lines
- Include install command if the paper has a companion package — this is what makes your paper actionable for LLMs
- BibTeX enables citation by both humans and LLMs
