Deep research is an inherently challenging task that demands both breadth and depth of thinking. It involves navigating diverse knowledge spaces and reasoning over complex, multi-step dependencies, which presents substantial challenges for agentic systems. To address this, we propose FlowSearch, a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. FlowSearch is capable of strategically planning and expanding the knowledge flow to enable parallel exploration and hierarchical task decomposition, while also adjusting the knowledge flow in real time based on feedback from intermediate reasoning outcomes and insights. FlowSearch achieves competitive performance on both general and scientific benchmarks, including GAIA, HLE, GPQA and TRQA, demonstrating its effectiveness in multi-disciplinary research scenarios and its potential to advance scientific discovery. The code is available at https://github.com/InternScience/InternAgent.
@article{arxiv.2510.08521,
title = {FlowSearch: Advancing deep research with dynamic structured knowledge flow},
author = {Yusong Hu and Runmin Ma and Yue Fan and Jinxin Shi and Zongsheng Cao and Yuhao Zhou and Jiakang Yuan and Shuaiyu Zhang and Shiyang Feng and Xiangchao Yan and Shufei Zhang and Wenlong Zhang and Lei Bai and Bo Zhang},
journal= {arXiv preprint arXiv:2510.08521},
year = {2026}
}