GASim: A Graph-Accelerated Hybrid Framework for Social Simulation

ACL ARR 2026 January Submission4716 Authors

Keywords: Social Simulation, Multi-Agent Systems, Agent Memory, Graph Neural Networks, Efficiency

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× 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.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: AI/LLM Agents, NLP Applications, Computational Social Science

Contribution Types: Approaches low compute settings-efficiency

Languages Studied: English

Submission Number: 4716