titleK-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts
authorsNahyun Lee, Dongkeun Yoon, Guijin Son, Geewook Kim, Dayoon Ko, Jeonghun Park, Haneul Yoo, Jaewon Cho, Junghun Park, Changyoon Lee, Kyochul Jang, Jaeyeon Kim, Eunsu Kim, Woojin Cho, Seungone Kim
date2026-06-01
venuearXiv (preprint, not peer reviewed)
idarXiv:2606.02404
affiliationCarnegie Mellon University
upvotes56 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractFrontier 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.

cite

@misc{lee2026k,
  title={K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts},
  author={Nahyun Lee and Dongkeun Yoon and Guijin Son and Geewook Kim and Dayoon Ko and Jeonghun Park and Haneul Yoo and Jaewon Cho and Junghun Park and Changyoon Lee and Kyochul Jang and Jaeyeon Kim and Eunsu Kim and Woojin Cho and Seungone Kim},
  year={2026},
  eprint={2606.02404},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2606.02404}
}