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%.

Xiucheng Xu, Bingbing Xu, Tian Xueyun, Zihe Huang, Rongxin Chen, Li Yunfan, Huawei Shen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.