发表机构
SpaceData Inc.; Graduate School of Artificial Intelligence and Science, Rikkyo University; Earth-Life Science Institute, Institute of Science Tokyo; Université Paris Cité, Institut de Physique du Globe de Paris, CNRS（斯페이스데이터公司; 立命馆大学人工智能与科学研究生院; 东京科学大学地球生命科学研究所; 巴黎西岱大学、巴黎地球物理研究所、法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出SD-AgentFoundry-2D和3D两款极简仿真基础，可在多系统本地运行，用于教育与快速原型设计，为学习生成式社会仿真及构建特定领域扩展提供便捷起点。
AI 中文摘要
大型语言模型（LLM）和视觉语言模型（VLM）正在扩展基于智能体的仿真中可表征的行为范围，但许多当代平台难以在普通计算机上研究、修改或运行。我们提出两种专为教育和快速原型设计打造的极简仿真基础：SD-AgentFoundry-2D提供二维多智能体环境，其中本地托管的LLM智能体可移动、通信、响应位置占用情况并遭遇空间局部化的火灾事件；SD-AgentFoundry-3D提供三维数字孪生环境，其中本地托管的VLM接收第一人称图像并生成自然语言移动指令。两个代码库均设计为可在macOS、Windows和Linux上本地运行，且刻意保留开放性以支持修改，而非作为成品应用开发。它们共同为学习生成式社会仿真及构建特定领域扩展提供了便捷的起点。
英文摘要
Large language models (LLMs) and vision-language models (VLMs) are expanding the range of behaviors that can be represented in agent-based simulations, but many contemporary platforms are difficult to study, modify, or run on ordinary computers. We present two intentionally minimal simulation foundations for education and rapid prototyping. SD-AgentFoundry-2D provides a two-dimensional multi-agent environment in which locally hosted LLM agents move, communicate, respond to place occupancy, and encounter spatially localized fire events. SD-AgentFoundry-3D provides a three-dimensional digital-twin environment in which a locally hosted VLM receives first-person images and produces natural-language movement instructions. Both codebases are designed to run locally on macOS, Windows, and Linux and are deliberately left open to modification rather than developed as finished applications. Together, they offer accessible starting points for learning about generative social simulation and for building domain-specific extensions.
CommentsGitHub Repositories: https://github.com/ryukih/SD-AgentFoundry-2D and https://github.com/ryukih/SD-AgentFoundry-3D
