cs.AI updates on arXiv.org 10月10日
FlowSearch:多智能体框架推动科研深度探索
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本文提出FlowSearch,一种多智能体框架,旨在通过构建和演进动态结构化知识流,驱动子任务执行和推理,从而解决深度研究中的挑战。FlowSearch在多个科研基准测试中取得优异表现,展示了其在多学科研究场景中的有效性和推进科学发现的潜力。

arXiv:2510.08521v1 Announce Type: new 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 https://github.com/Alpha-Innovator/InternAgent.

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多智能体框架 深度研究 知识流 科研效率 科学发现
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