| name | paper-reading |
| description | Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden assumptions, and connections to the user's other work. Use when the user shares an arXiv link, PDF, or paper title and asks to read / summarize / digest / TL;DR / analyze / review / critique / explain / break down a paper, asks about a paper's contributions / methods / results / equations / figures, wants to compare two papers side by side, or needs a reading note for their records.
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Paper Reading
Structured workflow for reading academic papers efficiently.
When to Use
- User shares an arXiv link or PDF and asks to read/summarize it
- User asks about a specific paper's contributions, methods, or results
- User wants a reading digest for their records
- User asks to compare a paper against related work
Reading Levels
Level 1: Quick Skim (2 min)
When: User just wants to know if a paper is worth reading
Output:
- Paper title, authors, venue, year
- One-paragraph summary (what problem, what method, what result)
- Key contribution in one sentence
- Relevance assessment to user's work
- Recommendation: Read / Skip / Skim only
Level 2: Standard Read (10 min)
When: User wants to understand the paper's approach
Output:
- Problem: What gap does this address?
- Method: How do they solve it? (with key technical details)
- Key innovation: What's genuinely new vs. incremental?
- Results: Main numbers + comparison to baselines
- Limitations: What they don't do, acknowledged or not
- Connections: How does this relate to user's active projects?
Level 3: Deep Analysis (30 min)
When: User is seriously considering building on this paper
Output:
- Everything from Level 2, plus:
- Detailed methodology: Step-by-step technical walkthrough
- Reproducibility assessment: Can you implement this from the paper alone?
- Experimental design critique: Are the baselines fair? Metrics appropriate?
- Hidden assumptions: What are they not saying?
- Extension opportunities: How could this be improved or adapted?
- Key equations/algorithms: Extracted and explained
- Figure analysis: What do the key figures actually show?
Workflow
Step 1: Obtain Paper
arXiv link → Download PDF, extract text
PDF file → Extract text directly
Paper title → Search Semantic Scholar → get arXiv link → download
Zotero item → Get from local library
Step 2: Read at Requested Level
Follow the appropriate level template above. When in doubt, start with Level 2.
Step 3: Store Digest
After reading, save the digest:
- Store structured summary to local dashboard
- If user confirms, add/update Zotero entry with notes
Step 4: Connect to Context
- Link to user's active projects if relevant
- Suggest follow-up papers (from references or "cited by")
- Note if this paper supports or contradicts prior reads
Reading Heuristics
For ML/AI papers:
- Jump to Table 1 (main results) first — if the numbers aren't impressive, calibrate expectations
- Check the ablation study — it reveals what actually matters in their method
- Read the limitations/future work section — often more honest than the intro
- Look at Appendix — important details are often buried there
For methods papers:
- Focus on Figure 1 (method overview) + Section 3 (method) + Table 1 (results)
- Skip related work on first pass — come back only if you need positioning context
For empirical papers:
- Focus on experimental setup, metrics, and statistical significance
- Check if baselines are fairly implemented (same hyperparameter search budget?)
- Look for cherry-picked examples in qualitative analysis
Paper Comparison Mode
When user asks to compare two papers:
| Aspect | Paper A | Paper B |
|--------------|------------------|------------------|
| Problem | | |
| Method | | |
| Data | | |
| Key metric | | |
| Advantage | | |
| Limitation | | |
