标题： 探索基于黑板架构的高级LLM多智能体系统 显示英文标题

标题： Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture

摘要： 在本文中，我们提出将黑板架构纳入大型语言模型多智能体系统（MASs）中，以便（1）具有不同角色的智能体可以在整个问题解决过程中共享所有信息和其他智能体的消息，（2）根据黑板的当前内容选择将采取行动的智能体，并（3）重复选择和执行轮次，直到在黑板上达成共识。我们开发了该提议的第一个实现，并在常识知识、推理和数学数据集上进行了实验。结果表明，我们的系统通过达到最佳平均性能，能够与最先进的静态和动态 MASs 相竞争，同时能够减少令牌的使用。我们的提议有潜力实现复杂和动态的问题解决，其中缺乏明确的结构或工作流程。 显示英文摘要

摘要： In this paper, we propose to incorporate the blackboard architecture into LLM multi-agent systems (MASs) so that (1) agents with various roles can share all the information and others' messages during the whole problem-solving process, (2) agents that will take actions are selected based on the current content of the blackboard, and (3) the selection and execution round is repeated until a consensus is reached on the blackboard. We develop the first implementation of this proposal and conduct experiments on commonsense knowledge, reasoning and mathematical datasets. The results show that our system can be competitive with the SOTA static and dynamic MASs by achieving the best average performance, and at the same time manage to spend less tokens. Our proposal has the potential to enable complex and dynamic problem-solving where well-defined structures or workflows are unavailable.