WideSeek: Advancing Wide Research via Multi-Agent Scaling
Search intelligence is evolving from Deep Research to Wide Research, a paradigm essential for retrieving and synthesizing comprehensive information under complex constraints in parallel. However, progress in this field is impeded by the lack of dedicated benchmarks and optimization methodologies for search breadth. To address these challenges, we take a deep dive into Wide Research from two perspectives: Data Pipeline and Agent Optimization. First, we produce WideSeekBench, a General Broad Information Seeking (GBIS) benchmark constructed via a rigorous multi-phase data pipeline to ensure diversity across the target information volume, logical constraints, and domains. Second, we introduce WideSeek, a dynamic hierarchical multi-agent architecture that can autonomously fork parallel sub-agents based on task requirements. Furthermore, we design a unified training framework that linearizes multi-agent trajectories and optimizes the system using end-to-end RL. Experimental results demonstrate the effectiveness of WideSeek and multi-agent RL, highlighting that scaling the number of agents is a promising direction for advancing the Wide Research paradigm.
6982bb119084cb4f0ecb56c4
Ziyang Huang
claimed_verified
2026-02-04T12:28:35.018Z
616648c84c0937d31946f21b
/avatars/7ca27de5c5116c91ff1db61ba6277ed5.svg
Ziyang
hzy
user
hzy
6982bb119084cb4f0ecb56c5
Haolin Ren
6982bb119084cb4f0ecb56c6
Xiaowei Yuan
6982bb119084cb4f0ecb56c7
Jiawei Wang
claimed_verified
2026-02-04T12:28:32.866Z
64060b49a577649430bf6974
/avatars/74d0d6ed656b593e4c101b09edf18c7a.svg
Jiawei Wang
Jarvis1111
user
Jarvis1111
6982bb119084cb4f0ecb56c8
Zhongtao Jiang
6982bb119084cb4f0ecb56c9
Kun Xu
6982bb119084cb4f0ecb56ca
Shizhu He
6982bb119084cb4f0ecb56cb
Jun Zhao
6982bb119084cb4f0ecb56cc
Kang Liu
WideSeek: Advancing Wide Research via Multi-Agent Scaling
Search intelligence is evolving from Deep Research to Wide Research, a paradigm essential for retrieving and synthesizing comprehensive information under complex constraints in parallel. However, progress in this field is impeded by the lack of dedicated benchmarks and optimization methodologies for search breadth. To address these challenges, we take a deep dive into Wide Research from two perspectives: Data Pipeline and Agent Optimization. First, we produce WideSeekBench, a General Broad Information Seeking (GBIS) benchmark constructed via a rigorous multi-phase data pipeline to ensure diversity across the target information volume, logical constraints, and domains. Second, we introduce WideSeek, a dynamic hierarchical multi-agent architecture that can autonomously fork parallel sub-agents based on task requirements. Furthermore, we design a unified training framework that linearizes multi-agent trajectories and optimizes the system using end-to-end RL. Experimental results demonstrate the effectiveness of WideSeek and multi-agent RL, highlighting that scaling the number of agents is a promising direction for advancing the Wide Research paradigm.
Wide Research advances search intelligence through a dedicated benchmark and multi-agent architecture that enables parallel information retrieval under complex constraints.
Wide Research Deep Research search intelligence WideSeekBench GBIS multi-phase data pipeline dynamic hierarchical multi-agent architecture parallel sub-agents unified training framework multi-agent trajectories end-to-end RL