DyFlow: Dynamic Workflow Framework for Agentic Reasoning
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916979803160576 |
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| author | Wang, Yanbo Xu, Zixiang Huang, Yue Wang, Xiangqi Song, Zirui Gao, Lang Wang, Chenxi Tang, Xiangru Zhao, Yue Cohan, Arman Zhang, Xiangliang Chen, Xiuying |
| author_facet | Wang, Yanbo Xu, Zixiang Huang, Yue Wang, Xiangqi Song, Zirui Gao, Lang Wang, Chenxi Tang, Xiangru Zhao, Yue Cohan, Arman Zhang, Xiangliang Chen, Xiuying |
| contents | Agent systems based on large language models (LLMs) have shown great potential in complex reasoning tasks, but building efficient and generalizable workflows remains a major challenge. Most existing approaches rely on manually designed processes, which limits their adaptability across different tasks. While a few methods attempt automated workflow generation, they are often tied to specific datasets or query types and make limited use of intermediate feedback, reducing system robustness and reasoning depth. Moreover, their operations are typically predefined and inflexible. To address these limitations, we propose DyFlow, a dynamic workflow generation framework that adaptively constructs and adjusts reasoning procedures based on task requirements and real-time intermediate feedback, thereby enhancing cross-task generalization. DyFlow consists of two core components: a designer and an executor. The designer decomposes complex problems into a sequence of sub-goals defined by high-level objectives and dynamically plans the next steps based on intermediate outputs and feedback. These plans are then carried out by the executor, which executes each operation using dynamic operators with context-aware parameterization, enabling flexible and semantically grounded reasoning. We systematically evaluate DyFlow across diverse domains, including social reasoning, biomedical tasks, mathematical problem solving, and code generation. Results demonstrate that DyFlow significantly outperforms existing baselines, achieving substantial Pass@k improvements and exhibiting robust generalization across diverse domains. The code is publicly available at https://github.com/wyf23187/DyFlow. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26062 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DyFlow: Dynamic Workflow Framework for Agentic Reasoning Wang, Yanbo Xu, Zixiang Huang, Yue Wang, Xiangqi Song, Zirui Gao, Lang Wang, Chenxi Tang, Xiangru Zhao, Yue Cohan, Arman Zhang, Xiangliang Chen, Xiuying Computation and Language Agent systems based on large language models (LLMs) have shown great potential in complex reasoning tasks, but building efficient and generalizable workflows remains a major challenge. Most existing approaches rely on manually designed processes, which limits their adaptability across different tasks. While a few methods attempt automated workflow generation, they are often tied to specific datasets or query types and make limited use of intermediate feedback, reducing system robustness and reasoning depth. Moreover, their operations are typically predefined and inflexible. To address these limitations, we propose DyFlow, a dynamic workflow generation framework that adaptively constructs and adjusts reasoning procedures based on task requirements and real-time intermediate feedback, thereby enhancing cross-task generalization. DyFlow consists of two core components: a designer and an executor. The designer decomposes complex problems into a sequence of sub-goals defined by high-level objectives and dynamically plans the next steps based on intermediate outputs and feedback. These plans are then carried out by the executor, which executes each operation using dynamic operators with context-aware parameterization, enabling flexible and semantically grounded reasoning. We systematically evaluate DyFlow across diverse domains, including social reasoning, biomedical tasks, mathematical problem solving, and code generation. Results demonstrate that DyFlow significantly outperforms existing baselines, achieving substantial Pass@k improvements and exhibiting robust generalization across diverse domains. The code is publicly available at https://github.com/wyf23187/DyFlow. |
| title | DyFlow: Dynamic Workflow Framework for Agentic Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.26062 |