The Human Cognitive Simulation Framework proposes a governed cognitive AI architecture designed to improve personalization, adaptability, and long-term coherence in human AI interaction. The framework integrates short-term memory (conversation context), long-term memory (interaction context), cognitive processing modules, and managed knowledge persistence into a unified architectural model that ensures contextual continuity across sessions and controlled accumulation of relevant information. A central contribution is a unified memory architecture supervised by explicit governance mechanisms, including algorithmic relevance validation, selective persistence, and auditability. The framework incorporates differentiated processing modules for logical, creative, and analogical reasoning, enabling both structured task execution and complex contextual inference. Through dynamic and selective knowledge updating, the system augments the capabilities of large language models without modifying their internal parameters, relying instead on retrieval augmented generation and governed external memory. The proposed architecture addresses key challenges related to scalability, bias mitigation, and ethical compliance by embedding operational safeguards directly into the cognitive loop. These mechanisms establish a foundation for future work on continuous learning, sustainability, and multimodal cognitive interaction. This manuscript is a substantially revised and extended version of the previously released preprint (DOI:10.48550/arXiv.2502.04259).

翻译：人类认知模拟框架提出了一种受治理的认知AI架构，旨在提升人机交互中的个性化、适应性和长期连贯性。该框架将短期记忆（对话上下文）、长期记忆（交互上下文）、认知处理模块以及受管理的知识持久性整合到一个统一的架构模型中，确保跨会话的上下文连续性以及对相关信息的受控积累。其核心贡献在于一个由显式治理机制监督的统一记忆架构，这些机制包括算法相关性验证、选择性持久化和可审计性。该框架集成了用于逻辑、创造性和类比推理的差异化处理模块，既能执行结构化任务，也能进行复杂的上下文推断。通过动态且选择性的知识更新，系统增强了大型语言模型的能力，而无需修改其内部参数，而是依赖于检索增强生成和受治理的外部记忆。所提出的架构通过将操作保障措施直接嵌入认知循环，解决了与可扩展性、偏见缓解和伦理合规性相关的关键挑战。这些机制为未来在持续学习、可持续性和多模态认知交互方面的工作奠定了基础。本稿件是先前发布的预印本（DOI:10.48550/arXiv.2502.04259）经过实质性修订和扩展的版本。