This paper presents a novel generative model, Collaborative Competitive Agents (CCA), which leverages the capabilities of multiple Large Language Models (LLMs) based agents to execute complex tasks. Drawing inspiration from Generative Adversarial Networks (GANs), the CCA system employs two equal-status generator agents and a discriminator agent. The generators independently process user instructions and generate results, while the discriminator evaluates the outputs, and provides feedback for the generator agents to further reflect and improve the generation results. Unlike the previous generative model, our system can obtain the intermediate steps of generation. This allows each generator agent to learn from other successful executions due to its transparency, enabling a collaborative competition that enhances the quality and robustness of the system's results. The primary focus of this study is image editing, demonstrating the CCA's ability to handle intricate instructions robustly. The paper's main contributions include the introduction of a multi-agent-based generative model with controllable intermediate steps and iterative optimization, a detailed examination of agent relationships, and comprehensive experiments on image editing. Code is available at \href{https://github.com/TiankaiHang/CCA}{https://github.com/TiankaiHang/CCA}.

该研究介绍了一种新颖的协同竞争代理生成模型（CCA），利用基于多个大型语言模型的代理来执行复杂任务。该模型通过两个同等地位的生成器代理和一个判别器代理，使生成器独立处理用户指令并生成结果，而判别器评估输出并为生成器代理提供反馈以进一步改善生成结果。与以前的模型不同，该系统可以获得生成的中间步骤，从而使每个生成器代理可以从其他成功的执行中学习，实现协同竞争以提高系统结果的质量和鲁棒性。本研究的主要重点是图像编辑，展示了CCA在处理复杂指令时的能力。该论文的主要贡献包括引入基于多个代理的生成模型，具有可控的中间步骤和迭代优化，对代理之间关系的详细研究，以及对图像编辑的综合实验。

CCA：协作竞争智能体图像编辑