为人工智能构建可解释的信念认知框架，支持自我反思与目标思考。
Theoretical Foundations for Semantic Cognition in Artificial Intelligence
- 将信念建模为动态语言表达的可导航结构
- 提出'认知真空'作为信念空间的初始状态
- 适用于大模型与混合智能体，提升可解释性
本文提出一种模块化认知架构，以形式化建模信念为结构化语义状态为基础。信念状态被定义为嵌入可导航流形中的动态语言表达集合，通过算子实现吸收、抽象、消解、记忆与内省。结合哲学、认知科学与神经科学，构建分层框架，使智能体具备自我调节的认知能力，实现有反思性的目标导向思维。核心是‘认知真空’——一类语义惰性认知状态，作为信念空间的概念起点。由此衍生出‘空塔’这一递归生成结构，由内部表征能力构建。理论框架可应用于符号系统与神经网络，包括大语言模型、混合智能体与自适应记忆架构。为构建能推理、记忆并结构化调控信念的智能体提供基础支撑。
原文摘要 · Abstract (English)
This monograph presents a modular cognitive architecture for artificial intelligence grounded in the formal modeling of belief as structured semantic state. Belief states are defined as dynamic ensembles of linguistic expressions embedded within a navigable manifold, where operators enable assimilation, abstraction, nullification, memory, and introspection. Drawing from philosophy, cognitive science, and neuroscience, we develop a layered framework that enables self-regulating epistemic agents capable of reflective, goal-directed thought. At the core of this framework is the epistemic vacuum: a class of semantically inert cognitive states that serves as the conceptual origin of belief space. From this foundation, the Null Tower arises as a generative structure recursively built through internal representational capacities. The theoretical constructs are designed to be implementable in both symbolic and neural systems, including large language models, hybrid agents, and adaptive memory architectures. This work offers a foundational substrate for constructing agents that reason, remember, and regulate their beliefs in structured, interpretable ways.
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