FlowSearch: Advancing deep research with dynamic structured knowledge flow
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911365279514624 |
|---|---|
| author | Hu, Yusong Ma, Runmin Fan, Yue Shi, Jinxin Cao, Zongsheng Zhou, Yuhao Yuan, Jiakang Zhang, Shuaiyu Feng, Shiyang Yan, Xiangchao Zhang, Shufei Zhang, Wenlong Bai, Lei Zhang, Bo |
| author_facet | Hu, Yusong Ma, Runmin Fan, Yue Shi, Jinxin Cao, Zongsheng Zhou, Yuhao Yuan, Jiakang Zhang, Shuaiyu Feng, Shiyang Yan, Xiangchao Zhang, Shufei Zhang, Wenlong Bai, Lei Zhang, Bo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FlowSearch: Advancing deep research with dynamic structured knowledge flow Hu, Yusong Ma, Runmin Fan, Yue Shi, Jinxin Cao, Zongsheng Zhou, Yuhao Yuan, Jiakang Zhang, Shuaiyu Feng, Shiyang Yan, Xiangchao Zhang, Shufei Zhang, Wenlong Bai, Lei Zhang, Bo Artificial Intelligence 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. |
| title | FlowSearch: Advancing deep research with dynamic structured knowledge flow |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.08521 |