Papers

About

LLMs often struggle to effectively process academic papers, leading to issues like inaccurate searches, hallucinated metadata, garbled math, and wasted tokens from raw PDF parsing. Papers provides a powerful solution by enabling comprehensive arXiv search and extracting paper content as clean, structured Markdown. This allows Large Language Models to accurately access and utilize academic information, complete with properly formatted math, figures, tables, and citations, ensuring precise answers and summaries.

Key Features

• Converts academic paper content to clean Markdown with proper formatting.
• Preserves math equations (LaTeX to $inline$ / $$block$$), figures, and tables.
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• Comprehensive arXiv search with full query syntax (title, author, abstract, category).
• Supports granular content extraction (abstract, body, appendix, or full paper).
• Ensures accurate citations and prevents hallucinated references.

Use Cases

• Enabling LLMs to accurately summarize academic papers and research findings.
• Retrieving specific sections or full content of arXiv papers for detailed analysis.
• Facilitating advanced, structured search across millions of arXiv publications for LLM context.