I have always been drawn to what I know, what I do not, and how to explore the gap between them. This leads me to ask how models can use external knowledge to solve increasingly challenging problems. I believe we already have plenty of knowledge to draw on, especially the knowledge that keeps evolving and accumulating. We need memory systems that models can manage on their own, so they can reuse prior knowledge instead of reasoning from scratch and avoid repeating past failures.
Such knowledge is hard to retrieve with existing methods, since it rarely looks similar on the surface. Still, humans (especially domain experts) intuitively know where to look in a vast search space and find connections that seem obvious only in hindsight, a kind of research taste explored in ScholarCatalyst. I aim to build models that find such connections for better decision making in open-ended, long-horizon problems.
To learn more about how I think beyond research, read my Blog.
Publication
ScholarCatalyst: A Benchmark for Retrieving Papers that Inspire New Research
Sohyeon Kim*, Yoonho Lee*, Bo Liu, Dayoon Ko, Rulin Shao, Seungone Kim, Graham Neubig, Pang Wei Koh, Aakanksha Chowdhery, Akari Asai, Omar Khattab, Yejin Choi, Gunhee Kim, Chelsea Finn Preprint 2026 WebsitePaperCodeData*equal contributionScholarCatalyst is a benchmark built from AI researchers’ firsthand accounts of what inspired their work. A paper that inspires a project may share little surface similarity with the question (right), while topically related work may not (left). Even strong retrieval systems, including agents, find only about half of such papers.
MULTI3IR: A Benchmark for Multi-perspective Multi-domain Multi-modal Information Retrieval
Seokwon Song, Sohyeon Kim, Gunhee Kim EMNLP 2026 Paper
When Is Enough Not Enough? Illusory Completion in Search Agents