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.

翻译：搜索智能正从深度研究向广度研究演进，后者是一种在复杂约束下并行检索与综合全面信息的关键范式。然而，该领域的发展因缺乏针对搜索广度的专用基准与优化方法而受阻。为应对这些挑战，我们从数据管道与智能体优化两个视角深入探究广度研究。首先，我们构建了WideSeekBench，这是一个通过严格多阶段数据管道构建的通用广域信息寻求基准，旨在确保目标信息量、逻辑约束与领域多样性。其次，我们提出了WideSeek，一种动态分层多智能体架构，能够根据任务需求自主分叉并行子智能体。此外，我们设计了一个统一的训练框架，将多智能体轨迹线性化，并利用端到端强化学习对系统进行优化。实验结果验证了WideSeek与多智能体强化学习的有效性，表明扩展智能体数量是推进广度研究范式的可行方向。