Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel
  Execution
Authors: name: Tianrui Qin, name: Qianben Chen, name: Sinuo Wang, name: He Xing, name: King Zhu, name: He Zhu, name: Dingfeng Shi, name: Xinxin Liu, name: Ge Zhang, name: Jiaheng Liu, name: Yuchen Eleanor Jiang, name: Xitong Gao, name: Wangchunshu Zhou
Organization: OPPO-Personal-AI-Lab

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.