Integrating LLM and Diffusion-Based Agents for Social Simulation

2025-10-18

AI Summary

Existing social simulation approaches face a dual bottleneck: rule-based agents lack semantic understanding, while LLM-based agents incur prohibitive computational overhead. This paper proposes a hybrid agent framework integrating large language models (LLMs) and diffusion models, featuring a modular dual-agent architecture—where the LLM agent performs semantic content parsing and reasoning, and the diffusion-model agent efficiently captures user-state evolution and social influence propagation. The framework jointly incorporates personalized user modeling, quantified social influence, and content-aware mechanisms to enable scalable, multi-factor-coupled simulation. Experiments on three real-world social datasets demonstrate substantial improvements in information diffusion prediction accuracy, achieving a favorable trade-off between precision and efficiency. Results validate the effectiveness and generalizability of the hybrid architecture for large-scale social simulation.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Simulating social information diffusion with semantic understanding
• Reducing computational costs in large-scale agent-based simulations
• Integrating LLM and diffusion models for hybrid simulation framework

Innovation

• Hybrid LLM and diffusion model agents
• Core semantic reasoning with efficient scaling
• Modular design balancing accuracy and cost