K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts
Authors: name: Nahyun Lee, name: Dongkeun Yoon, name: Guijin Son, name: Geewook Kim, name: Dayoon Ko, name: Jeonghun Park, name: Haneul Yoo, name: Jaewon Cho, name: Junghun Park, name: Changyoon Lee, name: Kyochul Jang, name: Jaeyeon Kim, name: Eunsu Kim, name: Woojin Cho, name: Seungone Kim
Organization: Carnegie Mellon University

Frontier model evaluations are shifting from foundational capabilities (e.g., instruction following and reasoning) toward compositional, agentic ones, but Korean agentic benchmarks remain scarce. We introduce K-BrowseComp, a web-browsing agent benchmark grounded in Korean contexts, consisting of 400 problems. The 300-problem K-BrowseComp-Verified subset is manually constructed and validated by native Korean speakers. On this subset, frontier LLMs, including GPT-5.5, DeepSeek-V4-Pro, and GLM-5.1, reach only 30.00--45.67\%, a substantial drop from BrowseComp, while Korean LLMs released through Korea's Proprietary AI Foundation Model program obtain only 0.00--10.33\%. We further construct a 100-problem synthetic split using hard few-shot exemplars and failure-mode-targeted generation to exploit the asymmetry between solving and creating web browsing problems. On the adversarially filtered synthetic diagnostic split, the strongest model reaches only 26.00\%, and we report this split separately as a targeted stress test. We publicly release our data and code.