WideSeek：通过多智能体规模化推进广泛研究

摘要

搜尋智能正從深度研究（Deep Research）向廣度研究（Wide Research）演進，這一範式對於在複雜約束條件下並行檢索與綜合全面資訊至關重要。然而，該領域的發展因缺乏針對搜尋廣度的專用基準與優化方法而受阻。為應對這些挑戰，我們從數據管道（Data Pipeline）和智能體優化（Agent Optimization）兩個維度深入探討廣度研究。首先，我們通過嚴謹的多階段數據管道構建了WideSeekBench——一個通用廣域資訊搜尋（GBIS）基準，確保其在目標資訊量、邏輯約束和領域分佈上的多樣性。其次，我們提出WideSeek動態分層多智能體架構，能夠根據任務需求自主派生並行子智能體。此外，我們設計了統一訓練框架，通過對多智能體軌跡進行線性化處理，並採用端到端強化學習實現系統優化。實驗結果驗證了WideSeek與多智能體強化學習的有效性，表明擴展智能體數量是推進廣度研究範式的重要方向。

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泛研究

WideSeek: Advancing Wide Research via Multi-Agent Scaling

摘要

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