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 sets under complex constraints in parallel. However, advancements in this field are impeded by the lack of dedicated benchmarks and optimization methodologies for search breadth. To address these challenges, we give 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 volume of target information, logical constraints, and domains. Second, we introduce WideSeek, a dynamic hierarchical multi-agent architecture that could 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 Wide Research paradigm.