High-fidelity agent initialization is crucial for credible Agent-Based Modeling across diverse domains. A robust framework should be Topic-Adaptive, capturing macro-level joint distributions while ensuring micro-level individual rationality. Existing approaches fall into two categories: static data-based retrieval methods that fail to adapt to unseen topics absent from the data, and LLM-based generation methods that lack macro-level distribution awareness, resulting in inconsistencies between micro-level persona attributes and reality. To address these problems, we propose HAG, a Hierarchical Agent Generation framework that formalizes population generation as a two-stage decision process. Firstly, utilizing a World Knowledge Model to infer hierarchical conditional probabilities to construct the Topic-Adaptive Tree, achieving macro-level distribution alignment. Then, grounded real-world data, instantiation and agentic augmentation are carried out to ensure micro-level consistency. Given the lack of specialized evaluation, we establish a multi-domain benchmark and a comprehensive PACE evaluation framework. Extensive experiments show that HAG significantly outperforms representative baselines, reducing population alignment errors by an average of 37.7% and enhancing sociological consistency by 18.8%.

翻译：高保真智能体初始化对于跨领域可信的基于智能体的建模至关重要。一个鲁棒的框架应具备主题自适应性，既能捕获宏观层面的联合分布，又能确保微观层面的个体合理性。现有方法主要分为两类：基于静态数据的检索方法无法适应数据中未见过的主题；而基于大语言模型的生成方法缺乏宏观分布感知能力，导致微观人物属性与现实之间存在不一致性。为解决这些问题，我们提出HAG——一种分层智能体生成框架，将群体生成形式化为两阶段决策过程。首先，利用世界知识模型推断分层条件概率以构建主题自适应树，实现宏观层面的分布对齐。随后，基于真实世界数据进行实例化与智能体增强，以确保微观层面的一致性。鉴于缺乏专门评估标准，我们建立了多领域基准测试与全面的PACE评估框架。大量实验表明，HAG显著优于代表性基线方法，平均将群体对齐误差降低37.7%，并将社会学一致性提升18.8%。