[2605.07692] GASim: A Graph-Accelerated Hybrid Framework for Social Simulation

Computer Science > Artificial Intelligence arXiv:2605.07692 (cs) [Submitted on 8 May 2026] Title:GASim: A Graph-Accelerated Hybrid Framework for Social Simulation Authors:Xuan Zhou, Yanhui Sun, Hantao Yao, Allen He, Yongdong Zhang, Wu Liu View a PDF of the paper titled GASim: A Graph-Accelerated Hybrid Framework for Social Simulation, by Xuan Zhou and 5 other authors View PDF HTML (experimental) Abstract:Large-scale social simulators are essential for studying complex social patterns. Prior work explores hybrid methods to scale up simulations, combining large language models (LLM)-based agents with numerical agent-based models (ABM). However, this incurs high latency due to expensive memory retrieval and sequential ABM execution. To address this challenge, we propose GASim, a graph-accelerated hybrid multi-agent framework for large-scale social simulations. For core agents driven by LLM, GASim introduces Graph-Optimized Memory (GOM) to replace intensive LLM-based retrieval pipelines with lightweight propagation over a sparse memory graph. For the majority of ordinary agents, GASim employs Graph Message Passing (GMP), substituting sequential ABM execution with parallel updates by fine-grained feature aggregation and Graph Attention Network. We further introduce Entropy-Driven Grouping (EDG) that coordinates this hybrid partitioning, leveraging information entropy to dynamically identify emergent core agents situated in information-diverse neighborhoods. Extensive experiment...