How I Built a Tool to Organize AI Conference Papers by Topic (and Why It Matters)

A developer built paperlist.ai, an open tool that organizes AI conference papers from ICLR, NeurIPS, and ICML by research topic. The tool uses automated topic assignment and manual curation to help researchers quickly find relevant papers without skimming through thousands of PDFs. The project highlights the value of information curation over content generation in AI research.

I maintain paperlist.ai, an open tool for browsing AI conference papers organized by research topic. How many ICLR papers are about RAG? What about agents or multimodal models? If you have ever tried to answer these questions by browsing a conference website, you know the pain. Thousands of papers, no topic grouping, and a PDF list that takes hours to skim. I built paperlist.ai https://paperlist.ai to fix this. Every year, AI conferences like ICLR, NeurIPS, and ICML publish thousands of accepted papers. Most researchers and developers only care about a tiny subset. But finding those relevant papers means: The information is there. It is just not organized. paperlist.ai groups papers by research topic: Each topic page lists relevant papers with their titles, authors, and links. No more opening 50 PDFs just to find the 3 that matter to your work. The site is built with Next.js and uses OpenNomos for tracking community contributions like article shares and SEO submissions. The paper data comes from conference proceedings, organized through a mix of automated topic assignment and manual curation. I wrote about this in more detail in a separate post https://opennomos.com , but the key insight is: organizing information is often more valuable than generating new content. An LLM can summarize a paper. But telling you which papers to read — that is a curation problem, not a generation problem. If you are catching up on ICLR 2026 papers, check out paperlist.ai https://paperlist.ai . RAG-related papers are already organized, with more topics coming. What tools do you use to stay on top of AI research? Would love to hear in the comments.