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.

Recently, large language models (LLMs) have demonstrated unprecedented capabilities in language generation, yet they still often produce incorrect information. Therefore, determining whether a text was generated by an LLM has become one of the factors that must be considered when evaluating its reliability. In this paper, we discuss methods to determine whether texts written in various languages were authored by humans or generated by LLMs. We have discovered that the classification accuracy significantly decreases for texts written in languages not observed during the training process, and we aim to address this issue. We propose a method to improve performance for unseen languages by using token-level predictive distributions extracted from various LLMs and text embeddings from a multilingual pre-trained langauge model. With the proposed method, we achieved third place out of 25 teams in Subtask B (binary multilingual machine-generated text detection) of Shared Task 1, with an F1 macro score of 0.7532.