Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs). However, achieving authentic alignment with human cognitive and behavioral patterns remains a critical challenge for these agents. We present HumanLLM, a framework treating psychological patterns as interacting causal forces. We construct 244 patterns from ~12,000 academic papers and synthesize 11,359 scenarios where 2--5 patterns reinforce, conflict, or modulate each other, with multi-turn conversations expressing inner thoughts, actions, and dialogue. Our dual-level checklists evaluate both individual pattern fidelity and emergent multi-pattern dynamics, achieving strong human alignment (r=0.90) while revealing that holistic metrics conflate simulation accuracy with social desirability. HumanLLM-8B outperforms Qwen3-32B on multi-pattern dynamics despite 4x fewer parameters, demonstrating that authentic anthropomorphism requires cognitive modeling -- simulating not just what humans do, but the psychological processes generating those behaviors. Our dataset, code, and model are available at: https://github.com/YJGoodbye2024/HumanLLM.git

翻译：大语言模型（LLMs）在推理与生成方面展现出卓越能力，成为高级人格模拟与角色扮演语言智能体（RPLAs）的基础。然而，实现与人类认知行为模式的真实对齐仍是这些智能体的关键挑战。我们提出HumanLLM框架，将心理模式视为相互作用的因果力量。基于约12,000篇学术论文构建244种模式，并合成11,359个场景（每个场景包含2~5种相互强化、冲突或调制的模式），以及表达内心想法、行动与对话的多轮交互。我们的双层检核清单既评估单一模式保真度，又评估涌现的多模式动态性，实现了高度的人类对齐（r=0.90），同时揭示整体指标会混淆模拟准确性与社会期望性。尽管参数规模仅为四分之一， HumanLLM-8B在多模式动态性上仍优于Qwen3-32B，证明真实的拟人化需要认知建模——不仅模拟人类的行为，更要模拟产生这些行为的心理过程。我们的数据集、代码和模型已发布在：https://github.com/YJGoodbye2024/HumanLLM.git