@inproceedings{zhou-etal-2026-gasim,
    title = "{GAS}im: A Graph-Accelerated Hybrid Framework for Social Simulation",
    author = "Zhou, Xuan  and
      Sun, Yanhui  and
      Yao, Hantao  and
      He, Allen  and
      Zhang, Yongdong  and
      Liu, Wu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.569/",
    pages = "12510--12528",
    ISBN = "979-8-89176-390-6",
    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{\texttimes} 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."
}