标题： 广义多智能体社会仿真框架 显示英文标题

标题： Generalized Multi-agent Social Simulation Framework

摘要： 多智能体社交互动显然受益于大型语言模型。 然而，当前的仿真系统仍然面临挑战，例如在扩展到各种场景时遇到困难，以及由于缺乏模块化设计而导致的可重用性差。 为了解决这些问题，我们设计并开发了一个模块化、面向对象的框架，通过分层结构有机地整合各种基础类，从而实现可扩展性和可重用性。 我们继承了该框架以实现常见的派生类。 此外，提出了一种记忆总结机制，用于从原始记忆数据中过滤和提炼相关信息，优先考虑具有上下文显著性的事件和互动。 通过选择和组合一些必要的派生类，我们定制了一个特定的仿真环境。 利用这个仿真环境，我们成功地模拟了社交媒体上的人类互动，复制了现实世界中的在线社交行为。 该项目的源代码将被发布并持续演进。 显示英文摘要

摘要： Multi-agent social interaction has clearly benefited from Large Language Models. However, current simulation systems still face challenges such as difficulties in scaling to diverse scenarios and poor reusability due to a lack of modular design. To address these issues, we designed and developed a modular, object-oriented framework that organically integrates various base classes through a hierarchical structure, harvesting scalability and reusability. We inherited the framework to realize common derived classes. Additionally, a memory summarization mechanism is proposed to filter and distill relevant information from raw memory data, prioritizing contextually salient events and interactions. By selecting and combining some necessary derived classes, we customized a specific simulated environment. Utilizing this simulated environment, we successfully simulated human interactions on social media, replicating real-world online social behaviors. The source code for the project will be released and evolve.