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%.

While RAG systems are designed to enhance factual fidelity by grounding LLMs in provided sources, the application of current watermarking techniques creates a conflict.These methods, being inherently fact-agnostic, force the model to deviate from the very source documents it is supposed to follow.This leads to "faithfulness hallucinations", which refer to a critical flaw where the generated output contradicts its own grounding context.Consequently, these watermarks undermine the core value of RAG, rendering even the most secure schemes untrustworthy for high-stakes applications.To resolve this RAG-specific conflict, we introduce the Dual Factual Shield (DFS), a three-stage post-hoc pipeline for factualitypreserving watermarking in RAG.It adopts a defense-in-depth design that combines a sourceanchored algorithmic safeguard for protecting critical tokens from retrieved context with prompt-based semantic guidance to mitigate factual corruption.Experiments show that our framework drastically reduces the Knowledge Corruption Rate (KCR), a new metric we introduce to quantify factual fidelity, while maintaining strong security and robustness, paving the way for responsible deployment of traceable AI in knowledge-critical domains.