Optimizing communication topology in LLM-based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of multi-round dialogues incurs high latency and computation. Motivated by the recent insights that evaluation and debate mechanisms can improve problem-solving in multi-agent systems, we propose TopoDIM, a framework for one-shot Topology generation with Diverse Interaction Modes. Designed for decentralized execution to enhance adaptability and privacy, TopoDIM enables agents to autonomously construct heterogeneous communication without iterative coordination, achieving token efficiency and improved task performance. Experiments demonstrate that TopoDIM reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. Moreover, the framework exhibits strong adaptability in organizing communication among heterogeneous agents. Code is available at: https://anonymous.4open.science/r/TopoDIM-8D35/

翻译：在基于大语言模型的多智能体系统中，优化通信拓扑对于实现集体智能至关重要。现有方法主要依赖于时空交互范式，其中多轮对话的顺序执行会导致高延迟和高计算开销。受近期关于评估与辩论机制可提升多智能体系统问题解决能力的见解启发，我们提出了TopoDIM——一个支持一次性生成多样化交互模式拓扑的框架。该框架专为去中心化执行而设计，以增强适应性与隐私性，使智能体能够在不依赖迭代协调的情况下自主构建异构通信，从而实现令牌效率与任务性能的提升。实验表明，相较于最先进的方法，TopoDIM在将总令牌消耗降低46.41%的同时，平均性能提升了1.50%。此外，该框架在组织异构智能体间通信方面展现出强大的适应性。代码已发布于：https://anonymous.4open.science/r/TopoDIM-8D35/