Title: BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
Primary area: datasets and benchmarks
Keywords: Large Language Models, Web Browsing, Retrieval and Reasoning, Benchmark Dataset, Chinese Web

Abstract: As large language models (LLMs) evolve into web-interacting agents, their ability to retrieve and reason over real-time information has become a crucial benchmark for general intelligence. However, existing benchmarks such as BrowseComp focus solely on English, neglecting the linguistic, infrastructural, and retrieval-specific challenges posed by other information ecosystems—particularly the Chinese web. We present BrowseComp-ZH, a high-difficulty, natively-constructed benchmark designed to assess LLM agents’ web browsing abilities in Chinese. Rather than translating from English, all questions in BrowseComp-ZH are written from scratch by native speakers to reflect authentic information-seeking behaviors and cultural contexts. The dataset comprises 289 multi-hop questions across 11 diverse domains, each reverse-engineered from a short, verifiable answer and filtered through a twostage quality control pipeline to ensure retrieval hardness and answer uniqueness. We evaluate over 20 leading LLMs and agentic search systems. Despite strong language and retrieval abilities, most models perform poorly: many score below 10% accuracy, and only a few exceed 20%. Even the best system achieves just 42.9% accuracy. These results highlight the considerable difficulty of BrowseComp-ZH, where success requires not only robust retrieval strategies but also advanced multihop reasoning and information reconciliation—abilities that remain challenging for current models. BrowseComp-ZH thus serves as a stress test for web-interactive LLMs beyond English, offering a rigorous and linguistically diverse evaluation framework to guide future research on multilingual agent capabilities.