Understanding the dynamic evolution of opinions, such as U.S. public attitudes toward China, is essential for assessing geopolitical risks. However, existing LLM-based multiagent simulators predominantly rely on static rules and fixed datasets, limiting their ability to capture the dynamic, event-driven nature of macro-level opinion shifts in real-world settings. To address this limitation, we propose an Event-Steered Multi-Agent Simulator (ES-MAS), in which significant events and daily news continuously drive opinion evolution through dynamic interactions among agents. We first construct the China-U.S. Relation Evolution (CURE) dataset, covering 20 quarters from 2021 to 2025, including 258 major events and over 14,000 daily news articles, and providing a comprehensive temporal foundation for modeling opinion dynamics. Building upon the CURE dataset, we propose a Dual-Stream Data Integration Engine (DSDIE) that aligns simulations with historical timelines via macro-level events while enabling personalized information exposure based on individual agent profiles and contextual signals. Furthermore, we design a News-Driven Dynamic Interaction (NDDI) module, which adaptively groups agents with shared news interests into localized interaction contexts, facilitating bottom-up consensus formation while mitigating the risk of isolated information cocoons. Experimental results on the CURE dataset demonstrate that ES-MAS substantially outperforms existing simulators in reproducing real-world historical trends, offering a scalable and effective framework for modeling dynamic opinion evolution.

翻译：理解民意（如美国公众对华态度）的动态演化对于评估地缘政治风险至关重要。然而，现有基于大语言模型的多智能体模拟器主要依赖静态规则和固定数据集，限制了其捕捉现实世界中宏观观点动态、事件驱动特性的能力。为解决此限制，我们提出一种事件驱动多智能体模拟器(ES-MAS)，其中重大事件和日常新闻通过智能体间的动态交互持续驱动观点演化。我们首先构建中美关系演化(CURE)数据集，覆盖2021至2025年共20个季度，包含258个重大事件及超过14000篇日常新闻文章，为建模观点动态提供全面的时间维度基础。基于CURE数据集，我们提出双流数据集成引擎(DSDIE)，通过宏观事件同步模拟与历史时间线，同时基于个体智能体画像和上下文信号实现个性化信息暴露。此外，我们设计新闻驱动动态交互(NDDI)模块，将共享新闻兴趣的智能体自适应分组为局部交互环境，促进自下而上的共识形成，同时缓解信息茧房隔离风险。在CURE数据集上的实验结果表明，ES-MAS在复现现实世界历史趋势方面显著优于现有模拟器，为建模动态观点演化提供了可扩展且高效的框架。