chanw-research Paper Analyzer
Overview
Perform deep analysis of a specific paper, generating structured notes that cover claims, methodology, experiment evaluation, strengths and limitations, and links to adjacent work.
Workflow
Step 1: Identify Paper
Accept input: arXiv ID (e.g., "2402.12345"), full ID ("arXiv:2402.12345"), paper title, or file path.
Step 2: Fetch Paper Content
curl -L "https://arxiv.org/pdf/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].pdf
curl -L "https://arxiv.org/e-print/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].tar.gz
curl -s "https://arxiv.org/abs/[PAPER_ID]" > /tmp/paper_analysis/arxiv_page.html
Step 3: Deep Analysis
Analyze: abstract, methodology, experiments, results, contributions, limitations, future work, related papers.
Step 4: Generate Note
python scripts/generate_note.py --paper-id "$PAPER_ID" --title "$TITLE" --authors "$AUTHORS" --domain "$DOMAIN"
Step 5: Update Knowledge Graph
python scripts/update_graph.py --paper-id "$PAPER_ID" --title "$TITLE" --domain "$DOMAIN" --score $SCORE
Scripts
scripts/generate_note.py— Generate structured note templatescripts/update_graph.py— Update paper relationship graph
Note Structure
The generated note includes: core info, abstract (EN/CN), research background, method overview with architecture figures, experiment results with tables, deep analysis, related paper comparison, tech roadmap positioning, future work, and comprehensive evaluation (0-10 scoring).
Dependencies
- Python 3.8+, PyYAML, requests
- Network access (arXiv)
Based on evil-read-arxiv — an automated paper reading workflow. MIT License.