Abstract
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 this https URL.
Abstract (translated)
深度研究是一项内在充满挑战的任务，需要广泛的思考和深入的分析。它涉及在多样化的知识领域中导航，并对复杂的多步骤依赖关系进行推理，这对代理系统来说是一个巨大的挑战。为了应对这一挑战，我们提出了FlowSearch，这是一个多代理框架，能够积极构建并演进动态结构化知识流以驱动子任务执行和推理。FlowSearch具备策略性地规划和扩展知识流的能力，从而实现并行探索和分层任务分解，并根据中间推理结果和见解实时调整知识流。 在包括GAIA、HLE、GPQA和TRQA在内的通用和科学基准测试中，FlowSearch表现出卓越的性能，这证明了它在跨学科研究场景中的有效性以及推进科学研究领域的潜力。代码可在提供的链接地址获取。
URL
https://arxiv.org/abs/2510.08521
