@inproceedings{wang-etal-2026-agent-gwo,
    title = "Agent-{GWO}: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models",
    author = "Wang, Xudong  and
      Zhang, Chaoning  and
      Li, Chenghao  and
      Chen, Shuxu  and
      Sun, Qigan  and
      Zhang, Jiaquan  and
      Puspitasari, Fachrina Dewi  and
      Kim, Tae-Ho  and
      Wei, Jiwei  and
      Zhang, Malu  and
      Wang, Guoqing  and
      Yang, Yang  and
      Shen, Heng Tao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.821/",
    pages = "16648--16670",
    ISBN = "979-8-89176-395-1",
    abstract = "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 ($\alpha$, $\beta$, and $\delta$) 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."
}