Flash-Searcher：基於DAG並行執行的快速高效網路代理

Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel Execution

September 29, 2025

作者: Tianrui Qin, Qianben Chen, Sinuo Wang, He Xing, King Zhu, He Zhu, Dingfeng Shi, Xinxin Liu, Ge Zhang, Jiaheng Liu, Yuchen Eleanor Jiang, Xitong Gao, Wangchunshu Zhou

cs.AI

摘要

大型語言模型（LLMs）在配備外部工具的情況下，已展現出在複雜推理任務中的卓越能力。然而，現有框架主要依賴於順序處理，導致執行效率低下，尤其是在需要大量工具互動的任務中。本文介紹了Flash-Searcher，一種新穎的平行代理推理框架，該框架從根本上重新構想了執行範式，從順序鏈轉向有向無環圖（DAGs）。Flash-Searcher將複雜任務分解為具有明確依賴關係的子任務，使得獨立推理路徑能夠並行執行，同時保持邏輯約束。通過動態工作流程優化，我們的框架基於中間結果持續精煉執行圖，有效整合了摘要模組。在多個基準測試中的全面評估表明，Flash-Searcher始終優於現有方法。具體而言，它在BrowseComp上達到了67.7%的準確率，在xbench-DeepSearch上達到了83%，同時與當前框架相比，代理執行步驟減少了高達35%。此外，當將這種平行推理管道蒸餾到單一模型中時，我們觀察到在不同骨幹架構上顯著的性能提升，這凸顯了我們方法的普遍適用性。因此，我們的工作代表了代理架構設計的重大進展，為複雜推理任務提供了一個更具可擴展性和效率的範式。

English

Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks when equipped with external tools. However, current frameworks predominantly rely on sequential processing, leading to inefficient execution particularly for tasks requiring extensive tool interaction. This paper introduces Flash-Searcher, a novel parallel agent reasoning framework that fundamentally reimagines the execution paradigm from sequential chains to directed acyclic graphs (DAGs). Flash-Searcher decomposes complex tasks into subtasks with explicit dependencies, enabling concurrent execution of independent reasoning paths while maintaining logical constraints. Through dynamic workflow optimization, our framework continuously refines the execution graph based on intermediate results, effectively integrating summary module. Comprehensive evaluations across multiple benchmarks demonstrate that Flash-Searcher consistently outperforms existing approaches. Specifically, it achieves 67.7% accuracy on BrowseComp and 83% on xbench-DeepSearch, while reducing agent execution steps by up to 35% compared to current frameworks. Furthermore, when distilling this parallel reasoning pipeline into single models, we observe substantial performance gains across diverse backbone architectures, underscoring the generalizability of our methodology. Our work thus represents a significant advance in agent architecture design, offering a more scalable and efficient paradigm for complex reasoning tasks.