@inproceedings{lee-etal-2026-k,
title = "K-{B}rowse{C}omp: A Web Browsing Agent Benchmark Grounded in {K}orean Contexts",
author = "Lee, Nahyun and
Yoon, Dongkeun and
Son, Guijin and
Kim, Geewook and
Ko, Dayoon and
Park, Jeonghun and
Yoo, Haneul and
Cho, Jaewon and
Park, Junghun and
Lee, Changyoon and
Jang, Kyochul and
Kim, Jaeyeon and
Kim, Eunsu and
Kim, Seungone",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.943/",
pages = "19876--19904",
ISBN = "979-8-89176-462-0",
abstract = "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 achieve at most 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 at https://github.com/prometheus-eval/K-BrowseComp."
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<abstract>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 achieve at most 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 at https://github.com/prometheus-eval/K-BrowseComp.</abstract>
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%0 Conference Proceedings
%T K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts
%A Lee, Nahyun
%A Yoon, Dongkeun
%A Son, Guijin
%A Kim, Geewook
%A Ko, Dayoon
%A Park, Jeonghun
%A Yoo, Haneul
%A Cho, Jaewon
%A Park, Junghun
%A Lee, Changyoon
%A Jang, Kyochul
%A Kim, Jaeyeon
%A Kim, Eunsu
%A Kim, Seungone
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lee-etal-2026-k
%X 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 achieve at most 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 at https://github.com/prometheus-eval/K-BrowseComp.
%U https://aclanthology.org/2026.findings-emnlp.943/
%P 19876-19904
Markdown (Informal)
[K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts](https://aclanthology.org/2026.findings-emnlp.943/) (Lee et al., Findings 2026)
ACL
- Nahyun 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, and Seungone Kim. 2026. K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19876–19904, Budapest, Hungary. Association for Computational Linguistics.