Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913048594219008 |
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| author | Wang, Xudong Zhang, Chaoning Li, Chenghao Chen, Shuxu Sun, Qigan Zhang, Jiaquan Puspitasari, Fachrina Dewi Kim, Tae-Ho Wei, Jiwei Zhang, Malu Wang, Guoqing Yang, Yang Shen, Heng Tao |
| author_facet | Wang, Xudong Zhang, Chaoning Li, Chenghao Chen, Shuxu Sun, Qigan Zhang, Jiaquan Puspitasari, Fachrina Dewi Kim, Tae-Ho Wei, Jiwei Zhang, Malu Wang, Guoqing Yang, Yang Shen, Heng Tao |
| contents | Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thought (CoT) have further elevated their performance in handling complex logical problems. Despite these advances, high-quality reasoning remains heavily reliant on manual static prompts and is sensitive to decoding configurations and task distributions, leading to performance fluctuations and limited transferability. Existing automatic prompt optimization methods typically adopt single-agent local search, failing to simultaneously optimize prompts and decoding hyperparameters within a unified framework to achieve stable global improvements. To address this limitation, we propose Agent-GWO, a dynamic prompt optimization framework for complex reasoning. Specifically, we unify prompt templates and decoding hyperparameters as inheritable agent configurations. By leveraging the leader-follower mechanism of the Grey Wolf Optimizer (GWO), we automatically select three leader agents ($α$, $β$, and $δ$) to guide the collaborative updates of the remaining agents, enabling iterative convergence toward robust optimal reasoning configurations that can be seamlessly integrated for inference. Extensive experiments on multiple mathematical and hybrid reasoning benchmarks across diverse LLM backbones show that Agent-GWO consistently improves accuracy and stability over existing prompt optimization methods. The code will be released publicly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18612 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models Wang, Xudong Zhang, Chaoning Li, Chenghao Chen, Shuxu Sun, Qigan Zhang, Jiaquan Puspitasari, Fachrina Dewi Kim, Tae-Ho Wei, Jiwei Zhang, Malu Wang, Guoqing Yang, Yang Shen, Heng Tao Neural and Evolutionary Computing Artificial Intelligence Machine Learning Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thought (CoT) have further elevated their performance in handling complex logical problems. Despite these advances, high-quality reasoning remains heavily reliant on manual static prompts and is sensitive to decoding configurations and task distributions, leading to performance fluctuations and limited transferability. Existing automatic prompt optimization methods typically adopt single-agent local search, failing to simultaneously optimize prompts and decoding hyperparameters within a unified framework to achieve stable global improvements. To address this limitation, we propose Agent-GWO, a dynamic prompt optimization framework for complex reasoning. Specifically, we unify prompt templates and decoding hyperparameters as inheritable agent configurations. By leveraging the leader-follower mechanism of the Grey Wolf Optimizer (GWO), we automatically select three leader agents ($α$, $β$, and $δ$) to guide the collaborative updates of the remaining agents, enabling iterative convergence toward robust optimal reasoning configurations that can be seamlessly integrated for inference. Extensive experiments on multiple mathematical and hybrid reasoning benchmarks across diverse LLM backbones show that Agent-GWO consistently improves accuracy and stability over existing prompt optimization methods. The code will be released publicly. |
| title | Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models |
| topic | Neural and Evolutionary Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.18612 |