WideSeek：通过多智能体规模化推进广度研究

摘要

搜索智能正从深度研究向广度研究演进，这种范式对于在复杂约束条件下并行检索与综合全面信息至关重要。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。为解决这些挑战，我们从数据管道和智能体优化两个维度对广度研究展开深入探索。首先，我们通过严格的多阶段数据管道构建了WideSeekBench——一个通用广域信息搜寻基准，确保目标信息量、逻辑约束和领域维度的多样性。其次，我们提出WideSeek动态分层多智能体架构，能够根据任务需求自主派发并行子智能体。此外，我们设计了统一训练框架，通过线性化多智能体轨迹并采用端到端强化学习进行系统优化。实验结果表明WideSeek与多智能体强化学习的有效性，证明扩展智能体数量是推进广度研究范式的可行方向。

English

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.

WideSeek：通过多智能体规模化推进广度研究

WideSeek: Advancing Wide Research via Multi-Agent Scaling

摘要

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