构建模块化框架，模拟社交媒体中人类互动行为。
Generalized Multi-agent Social Simulation Framework
- 采用分层模块设计，提升多场景适应性与代码复用性。
- 通过记忆摘要机制筛选关键社交事件，增强模拟真实性。
- 适合研究社会行为建模与人机交互的学者与开发者。
多智能体社会互动已从大语言模型中获益显著，但当前仿真系统仍面临难以扩展至多样化场景、复用性差等问题，主要源于缺乏模块化设计。为此，我们设计并开发了一种模块化、面向对象的框架，通过分层结构有机整合各类基础类，实现高可扩展性与可复用性。通过继承该框架，可快速构建常见派生类。此外，提出一种记忆摘要机制，从原始记忆数据中过滤并提炼相关信息，优先保留具有上下文重要性的事件与互动。通过选择并组合必要的派生类，可定制特定仿真环境。利用该环境，成功模拟了社交媒体上的人类互动，复现了真实世界中的在线社交行为。项目源码将公开并持续演进。
原文摘要 · Abstract (English)
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.
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