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 state-of-the-art 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/Alpha-Innovator/InternAgent.

翻译：深度研究本质上是一项极具挑战性的任务，它要求思维的广度和深度。它涉及在不同的知识空间中导航，并对复杂的多步骤依赖关系进行推理，这对智能体系统提出了重大挑战。为解决此问题，我们提出了FlowSearch，一个多智能体框架，它主动构建并演化动态结构化知识流，以驱动子任务执行和推理。FlowSearch能够策略性地规划和扩展知识流，以实现并行探索和层次化任务分解，同时还能根据中间推理结果和洞见的反馈实时调整知识流。FlowSearch在通用和科学基准测试（包括GAIA、HLE、GPQA和TRQA）上均取得了最先进的性能，证明了其在多学科研究场景中的有效性及其推动科学发现的潜力。代码可在 https://github.com/Alpha-Innovator/InternAgent 获取。