Don't read papers from start to finish.
Invest only as much time as the paper deserves.
The 3-pass method gives you a decision framework:
Should I even read this paper?
What are they claiming?
Do I believe them?
A 3-sentence summary. If you can't write it after Pass 1 — the paper is poorly written or not worth your time.
💡 Figure captions are gold. Most papers tell their entire story through 2–3 figures. Read captions before reading equations.
Filled paper notes template. You should be able to explain this paper to a colleague after Pass 2.
💡 Read experiments critically. Which dataset shows the weakest improvement? Which baseline is closest to theirs? These are the cracks.
Reserve Pass 3 for: papers in your direct research area, papers you plan to reproduce, or papers you're reviewing.
Most papers don't need Pass 3. After Pass 2 you already have 90% of what you need.
LLM hallucination → noisy RAG → K-RagRec fixes this with KG subgraph retrieval. Relevant to me. Continue.
Decode Eq. 3 (hop-field indexing), Eq. 5 (retrieval). Notice LLaMA-3 improvements are smaller. Spot missing traditional RS baselines. Fill paper notes.
Is the GNN indexing actually novel vs GraphSAGE? Is p=50% threshold justified? Would this work without a KG?
Figure captions tell the whole story. If you can't write 3 sentences after — stop.
Fill your notes template. Read experiments critically. This is enough for 90% of papers.
Reserve for papers you'll reproduce, review, or extend. Don't default here.
Next: M2 · L3 — Critical Questions to Ask Every Paper