[2601.05656] HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation

Computer Science > Artificial Intelligence arXiv:2601.05656 (cs) [Submitted on 9 Jan 2026 (v1), last revised 6 Apr 2026 (this version, v3)] Title:HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation Authors:Rongxin Chen, Tianyu Wu, Bingbing Xu, Jiatang Luo, Xiucheng Xu, Huawei Shen View a PDF of the paper titled HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation, by Rongxin Chen and 4 other authors View PDF HTML (experimental) Abstract: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 con...