标题： D3MAS：分解、推导和分发以增强多智能体系统中的知识共享 显示英文标题

标题： D3MAS: Decompose, Deduce, and Distribute for Enhanced Knowledge Sharing in Multi-Agent Systems

摘要： 由大型语言模型驱动的多智能体系统在协作解决问题方面表现出强大的能力。 然而，这些系统存在大量的知识冗余。 智能体会在检索和推理过程中重复努力。 这种低效率源于一个更深层次的问题：当前架构缺乏机制来确保在每个操作阶段智能体仅共享最小足够的信息。 实证分析显示，在智能体通信中平均知识重复率为47.3%。 我们提出了 D3MAS（分解、推导和分发），一种通过结构设计而非显式优化来解决冗余的分层协调框架。 该框架通过三个协调层组织协作。 任务分解在早期过滤不相关的子问题。 协作推理捕捉跨智能体的互补推理路径。 分布式内存提供对非冗余知识的访问。 这些层通过统一异构图中的结构化消息传递进行协调。 这种跨层对齐确保信息与实际任务需求保持一致。 在四个具有挑战性的数据集上的实验表明，D3MAS始终将推理准确性提高8.7%至15.6%，并平均减少46%的知识冗余。 显示英文摘要

摘要： Multi-agent systems powered by large language models exhibit strong capabilities in collaborative problem-solving. However, these systems suffer from substantial knowledge redundancy. Agents duplicate efforts in retrieval and reasoning processes. This inefficiency stems from a deeper issue: current architectures lack mechanisms to ensure agents share minimal sufficient information at each operational stage. Empirical analysis reveals an average knowledge duplication rate of 47.3\% across agent communications. We propose D3MAS (Decompose, Deduce, and Distribute), a hierarchical coordination framework addressing redundancy through structural design rather than explicit optimization. The framework organizes collaboration across three coordinated layers. Task decomposition filters irrelevant sub-problems early. Collaborative reasoning captures complementary inference paths across agents. Distributed memory provides access to non-redundant knowledge. These layers coordinate through structured message passing in a unified heterogeneous graph. This cross-layer alignment ensures information remains aligned with actual task needs. Experiments on four challenging datasets show that D3MAS consistently improves reasoning accuracy by 8.7\% to 15.6\% and reduces knowledge redundancy by 46\% on average.