@inproceedings{hu-etal-2026-flowsearch,
title = "{F}low{S}earch: Advancing Deep Research with Dynamic Structured Knowledge Flow",
author = "Hu, Yusong and
Ma, Runmin and
Fan, Yue and
Shi, Jinxin and
Cao, Zongsheng and
Zhou, Yuhao and
Yuan, Jiakang and
Zhang, Shuaiyu and
Feng, Shiyang and
Yan, Xiangchao and
Zhang, Shufei and
Zhang, Wenlong and
Bai, Lei and
Zhang, Bo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.971/",
pages = "21212--21245",
ISBN = "979-8-89176-390-6",
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 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 will be available."
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<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 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 will be available.</abstract>
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%0 Conference Proceedings
%T FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow
%A Hu, Yusong
%A Ma, Runmin
%A Fan, Yue
%A Shi, Jinxin
%A Cao, Zongsheng
%A Zhou, Yuhao
%A Yuan, Jiakang
%A Zhang, Shuaiyu
%A Feng, Shiyang
%A Yan, Xiangchao
%A Zhang, Shufei
%A Zhang, Wenlong
%A Bai, Lei
%A Zhang, Bo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F hu-etal-2026-flowsearch
%X 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 will be available.
%U https://aclanthology.org/2026.acl-long.971/
%P 21212-21245
Markdown (Informal)
[FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow](https://aclanthology.org/2026.acl-long.971/) (Hu et al., ACL 2026)
ACL
- Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, and Bo Zhang. 2026. FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21212–21245, San Diego, California, United States. Association for Computational Linguistics.