The latest breakthroughs in Large Language Models (LLMs), eg., ChatDev, have catalyzed profound transformations, particularly through multi-agent collaboration for software development. LLM agents can collaborate in teams like humans, and follow the waterfall model to sequentially work on requirements analysis, development, review, testing, and other phases to perform autonomous software generation. However, for an agent team, each phase in a single development process yields only one possible outcome. This results in the completion of only one development chain, thereby losing the opportunity to explore multiple potential decision paths within the solution space. Consequently, this may lead to obtaining suboptimal results. To address this challenge, we introduce Cross-Team Collaboration (CTC), a scalable multi-team framework that enables orchestrated teams to jointly propose various decisions and communicate with their insights in a cross-team collaboration environment for superior content generation. Experimental results in software development reveal a notable increase in quality compared to state-of-the-art baselines, underscoring the efficacy of our framework. The significant improvements in story generation demonstrate the promising generalization ability of our framework across various domains. We anticipate that our work will guide LLM agents towards a cross-team paradigm and contribute to their significant growth in but not limited to software development. The code and data will be available at https://github.com/OpenBMB/ChatDev.

翻译：大型语言模型（LLM）的最新突破（例如ChatDev）已引发深刻变革，尤其体现在通过多智能体协作进行软件开发方面。LLM智能体能够像人类一样在团队中协作，并遵循瀑布模型依次执行需求分析、开发、评审、测试等阶段，从而实现自主软件生成。然而，对于单个智能体团队而言，单一开发流程中的每个阶段仅产生一种可能结果，这导致只能完成一条开发链，从而丧失了在解空间中探索多种潜在决策路径的机会。因此，这可能使最终结果陷入局部最优。为应对这一挑战，我们提出了跨团队协作（CTC）——一种可扩展的多团队框架，该框架能够使多个协同团队在跨团队协作环境中共同提出多样化决策，并通过交流洞见以生成更优质的内容。软件开发实验结果表明，相较于现有先进基线方法，生成质量显著提升，印证了本框架的有效性。在故事生成任务中观察到的显著改进，进一步证明了本框架在不同领域具备良好的泛化能力。我们预期本工作将引导LLM智能体迈向跨团队协作范式，并推动其在软件开发及其他领域实现重要进展。相关代码与数据将在https://github.com/OpenBMB/ChatDev发布。