DeepWideSearch: Benchmarking Depth and Width in Agentic Information
  Seeking
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.
68fad42ef158a71c5a2f57d0
Tian Lan
claimed_verified
2025-10-24T16:12:50.133Z
627ca439a09edfe62239c671
https://cdn-avatars.huggingface.co/v1/production/uploads/1652335660508-noauth.jpeg
Tian Lan
user
GMFTBY
68fad42ef158a71c5a2f57d1
Bin Zhu
68fad42ef158a71c5a2f57d2
Qianghuai Jia
68fad42ef158a71c5a2f57d3
Junyang Ren
68fad42ef158a71c5a2f57d4
Haijun Li
68fad42ef158a71c5a2f57d5
Longyue Wang
68fad42ef158a71c5a2f57d6
Zhao Xu
68fad42ef158a71c5a2f57d7
Weihua Luo
68fad42ef158a71c5a2f57d8
Kaifu Zhang
DeepWideSearch: Benchmarking Depth and Width in Agentic Information
  Seeking
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.
DeepWideSearch is a benchmark that evaluates agents' ability to integrate deep reasoning and wide-scale information collection, revealing significant challenges and limitations in current agent architectures.
deep reasoning multi-hop retrieval wide-scale information collection DeepWideSearch benchmark information-seeking tasks reflection internal knowledge retrieval context overflow