Title: Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Models, dynamic prompt optimization, multi-agent collaboration, Grey Wolf Optimizer

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation, yet their performance in complex reasoning tasks remains limited. A central challenge lies in their heavy reliance on manually designed static prompts, which are costly to engineer, lack flexibility, and often fail to generalize across diverse tasks. In this work, we propose Agent-GWO, a dynamic prompt optimization framework that leverages collaboration among multiple LLM-based agents and the Grey Wolf Optimizer (GWO). Instead of fine-tuning model parameters, Agent-GWO enhances reasoning by iteratively refining task-specific prompts through cooperative optimization. Each agent is modeled as a ``wolf,'' guided by its hyperparameters and reasoning template. Through GWO’s hierarchical leader--follower mechanism, top-performing leader agents ($\alpha$, $\beta$, and $\delta$) guide the evolution of other agents, enabling the population to converge toward robust and effective reasoning strategies. Extensive experiments across mathematical reasoning, hybrid reasoning, and domain-specific applications (e.g., social sciences, medical diagnostics, and decision support) demonstrate the effectiveness of our approach. For example, on GPT-4.1-mini, Agent-GWO improves GSM8K accuracy by 8.7\% (from 88.2\% to 96.9\%) and MMLU accuracy by 12.9\% (from 66.9\% to 79.8\%).

TL;DR: We propose Agent-GWO, a parameter-free framework that uses Grey Wolf Optimizer to refine prompts through multi-agent collaboration, improving reasoning accuracy in large language models.