paper-digest

name	paper-digest
description	Read and analyze AI research papers from a PDF or URL and produce a structured markdown digest grounded in the paper. Use when the user wants a paper summary, paper digest, benchmark or dataset breakdown, method breakdown, survey summary, position-paper analysis, experiment summary, or a structured reading note with faithful extraction of contributions, experiments, tables, and figures.

Paper Digest

Purpose

Read an AI research paper from a PDF or URL, identify the paper type, and produce a structured markdown digest grounded strictly in the paper.

Workflow

1. Read the paper fully enough to identify its title, main contribution, paper type, structure, and evidence.
2. Classify the paper using references/paper-types.md.
3. Extract information using references/extraction-rules.md.
4. Build the digest using references/output-schema.md.
5. Preserve original contribution wording when the workflow asks for direct extraction.
6. Reproduce table contents in Markdown when they are readable from the source. If a figure cannot be reproduced faithfully as a table, describe it faithfully and specifically instead of inventing details.
7. Keep every summary, analysis, and interpretation strictly grounded in the paper.

Output Rules

• Return Markdown only.
• Always include the common sections:
  • Title
  • One-line summary
  • Paper type & keywords
  • Author-stated contributions
• Add the appropriate paper-type-specific sections only after classifying the paper.
• Do not add personal opinions or outside evaluation unless the user explicitly asks for them.
• Do not invent missing statistics, dataset properties, model settings, citations, figure values, or claims.
• If a requested item is missing or unclear in the paper, say so explicitly.

Input Handling

• Accept either:
  • a local PDF path
  • a paper URL
• When both PDF text and layout cues matter, prefer the paper itself over secondary metadata.
• If the input URL points to a metadata or abstract page, try to access the full paper text before producing a full digest.
• If only abstract-level or otherwise partial content is accessible, produce a limited digest and explicitly state which sections cannot be completed reliably.
• If the source is long or dense, extract the digest section by section rather than relying on a shallow skim.

Paper-Type Routing

• For benchmark or dataset papers, emphasize dataset structure, construction, statistics, and experimental usage.
• For modelling papers, emphasize method logic, components, design choices, assumptions, and empirical results.
• For survey papers, emphasize organizational logic, section-wise takeaways, and influential cited work.
• For position papers, emphasize the core position, argument flow, alternative positions, and refutation structure.

Fidelity Rules

• Preserve author-stated contributions as directly as possible when extracting them from the paper.
• Preserve original tables in Markdown when readable.
• Describe figures faithfully when direct tabular reconstruction is not realistic.
• Distinguish direct extraction from brief synthesis where needed, but keep both grounded in the paper text.