If you follow ML research, you know the feeling: ICLR, NeurIPS, ICML and CVPR each drop thousands of papers, and your "to-read" list quietly turns into a graveyard. Here's the workflow that finally worked for me. It's less about reading more and more about deciding faster.
1. Go topic-first, not conference-first
Browsing an entire conference proceedings front to back is how you burn out. Instead I start from a topic and pull the relevant papers across conferences and years.
Lately I've been using Paper List for this. You pick a topic (say, Retrieval-Augmented Generation) and it shows the scope up front, e.g. a few hundred papers across ~13 conferences and 5 years, before you dive in. Seeing the size of the space first genuinely changes how you budget attention.
2. Triage with a 3-pass skim
