DeepWideSearch: Benchmarking Depth and Width in Agentic Information
  Seeking
Authors: name: Tian Lan, name: Bin Zhu, name: Qianghuai Jia, name: Junyang Ren, name: Haijun Li, name: Longyue Wang, name: Zhao Xu, name: Weihua Luo, name: Kaifu Zhang
Organization: ATH-MaaS

Current search agents fundamentally lack the ability to simultaneously
perform deep reasoning over multi-hop retrieval and
wide-scale information collection-a critical deficiency for real-world
applications like comprehensive market analysis and business development. To
bridge this gap, we introduce DeepWideSearch, the first benchmark explicitly
designed to evaluate agents to integrate depth and width in information
seeking. In DeepWideSearch, agents must process a large volume of data, each
requiring deep reasoning over multi-hop retrieval paths. Specifically, we
propose two methods to converse established datasets, resulting in a curated
collection of 220 questions spanning 15 diverse domains. Extensive experiments
demonstrate that even state-of-the-art agents achieve only 2.39% average
success rate on DeepWideSearch, highlighting the substantial challenge of
integrating depth and width search in information-seeking tasks. Furthermore,
our error analysis reveals four failure modes: lack of reflection, overreliance
on internal knowledge, insufficient retrieval, and context overflow-exposing
key limitations in current agent architectures. We publicly release
DeepWideSearch to catalyze future research on more capable and robust
information-seeking agents.