Integrating LLM and Diffusion-Based Agents for Social Simulation

• URL: http://arxiv.org/abs/2510.16366v1
• Date: Sat, 18 Oct 2025 06:23:22 GMT
• Title: Integrating LLM and Diffusion-Based Agents for Social Simulation
• Authors: Xinyi Li, Zhiqiang Guo, Qinglang Guo, Hao Jin, Weizhi Ma, Min Zhang,
• Abstract summary: We propose a hybrid simulation framework that strategically integrates large language model (LLM)-driven agents with diffusion model-based agents.<n>Our framework outperforms existing methods in prediction accuracy, validating the effectiveness of its modular design.
• Score: 28.21329943306884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agent-based social simulation provides a valuable methodology for predicting social information diffusion, yet existing approaches face two primary limitations. Traditional agent models often rely on rigid behavioral rules and lack semantic understanding of textual content, while emerging large language model (LLM)-based agents incur prohibitive computational costs at scale. To address these challenges, we propose a hybrid simulation framework that strategically integrates LLM-driven agents with diffusion model-based agents. The framework employs LLM-based agents to simulate a core subset of users with rich semantic reasoning, while a diffusion model handles the remaining population efficiently. Although the two agent types operate on disjoint user groups, both incorporate key factors including user personalization, social influence, and content awareness, and interact through a coordinated simulation process. Extensive experiments on three real-world datasets demonstrate that our framework outperforms existing methods in prediction accuracy, validating the effectiveness of its modular design.

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