DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent
Deep-research agents are capable of executing multi-step web exploration, targeted retrieval, and sophisticated question answering. Despite their powerful capabilities, deep-research agents face two critical bottlenecks: (1) the lack of large-scale, challenging datasets with real-world difficulty, and (2) the absence of accessible, open-source frameworks for data synthesis and agent training. To bridge these gaps, we first construct DeepResearch-9K, a large-scale challenging dataset specifically designed for deep-research scenarios built from open-source multi-hop question-answering (QA) datasets via a low-cost autonomous pipeline. Notably, it consists of (1) 9000 questions spanning three...
| Authors | Tongzhou Wu, Yuhao Wang, Xinyu Ma et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 March 2026 |
| Last updated at source | 20 June 2026 |
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