{"ID":2834484,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01610","arxiv_id":"2512.01610","title":"Agent-Kernel: A MicroKernel Multi-Agent System Framework for Adaptive Social Simulation Powered by LLMs","abstract":"Multi-Agent System (MAS) developing frameworks serve as the foundational infrastructure for social simulations powered by Large Language Models (LLMs). However, existing frameworks fail to adequately support large-scale simulation development due to inherent limitations in adaptability, configurability, reliability, and code reusability. For example, they cannot simulate a society where the agent population and profiles change over time. To fill this gap, we propose Agent-Kernel, a framework built upon a novel society-centric modular microkernel architecture. It decouples core system functions from simulation logic and separates cognitive processes from physical environments and action execution. Consequently, Agent-Kernel achieves superior adaptability, configurability, reliability, and reusability. We validate the framework's superiority through two distinct applications: a simulation of the Universe 25 (Mouse Utopia) experiment, which demonstrates the handling of rapid population dynamics from birth to death; and a large-scale simulation of the Zhejiang University Campus Life, successfully coordinating 10,000 heterogeneous agents, including students and faculty.","short_abstract":"Multi-Agent System (MAS) developing frameworks serve as the foundational infrastructure for social simulations powered by Large Language Models (LLMs). However, existing frameworks fail to adequately support large-scale simulation development due to inherent limitations in adaptability, configurability, reliability, an...","url_abs":"https://arxiv.org/abs/2512.01610","url_pdf":"https://arxiv.org/pdf/2512.01610v1","authors":"[\"Yuren Mao\",\"Peigen Liu\",\"Xinjian Wang\",\"Rui Ding\",\"Jing Miao\",\"Hui Zou\",\"Mingjie Qi\",\"Wanxiang Luo\",\"Longbin Lai\",\"Kai Wang\",\"Zhengping Qian\",\"Peilun Yang\",\"Yunjun Gao\",\"Ying Zhang\"]","published":"2025-12-01T12:30:58Z","proceeding":"cs.MA","tasks":"[\"cs.MA\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}