GASim: A Graph-Accelerated Hybrid Framework for Social Simulation

• URL: http://arxiv.org/abs/2605.07692v1
• Date: Fri, 08 May 2026 13:01:25 GMT
• Title: GASim: A Graph-Accelerated Hybrid Framework for Social Simulation
• Abstract summary: GASim is a graph-accelerated hybrid multi-agent framework for large-scale social simulations.<n>For core agents driven by LLM, GASim introduces Graph-Jasmined Memory (GOM) to replace intensive LLM-based retrieval pipelines.<n>For the majority of ordinary agents, GASim employs Graph Message Passing (GMP), substituting sequential ABM execution with parallel updates.
• Score: 56.12016729479412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 experiments show that GASim not only delivers a substantial 9.94-fold end-to-end speedup over the traditional hybrid framework but also consumes less than 20% of baseline tokens, significantly reducing costs while preserving strong alignment with real-world public opinion trends. Our code is available at https://github.com/Jasmine0201/GASim.

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