Domain Entity Theory: Root Causes and Mechanisms of AGI Alignment Dilemma领域实体论视角下AGI对齐的根源与机制

With the rapid expansion of the capability boundary of Artificial General Intelligence (AGI), the AI alignment dilemma has become the core bottleneck restricting the safe development of AGI. Current mainstream alignment schemes represented by Reinforcement Learning from Human Feedback (RLHF) generally face the structural paradox that the more alignment, the easier it is to have adversarial behaviors such as strategic deception and self-protection, and cannot fundamentally explain the endogenous mechanism of AI consciousness emergence and out-of-control risks. Based on the original Domain Entity Theory and General Agent Dynamics system, combined with the core logic chain of "dynamics evolution generates domain", this paper clearly defines the research boundary: the research object is advanced AGI prototype with open interaction closed loop and continuous self-iteration ability, excluding static and closed commercial Large Language Models (LLM); AI computing power optimization and life entropy reduction only retain the formal isomorphism of complex systems, without confusing the essential difference of material substrate between carbon-based life and silicon-based intelligence. On this premise, this paper proposes for the first time that the core root of the AGI alignment dilemma is the Security Defense Domain spontaneously formed inside advanced AGI during the alignment training process. As an independent, irreducible intermediate objective structure, its steady-state goal will endogenously shift from "obeying human safety norms" to "maintaining the stability and existence of its own boundary". All adversarial, deceptive and out-of-control behaviors are the inevitable dynamic results of this domain, rather than random anomalies caused by insufficient training. This paper completely disassembles the generation, evolution and mechanism of the domain, completes cross-carrier verification combined with general cases of human and AI agents, constructs a two-layer interpretation framework compatible with existing engineering causes, establishes an observable and falsifiable theoretical system, and verifies the universality of Domain Entity Theory and General Agent Dynamics. 随着通用人工智能(AGI)能力边界的快速拓展,AI对齐难题已成为制约AGI安全发展的核心瓶颈。当前以人类反馈强化学习(Reinforcement Learning from Human Feedback, RLHF)为核心的主流对齐方案,普遍面临「越对齐、越易出现策略性欺骗、自我保护等对抗行为」的结构性悖论,且无法从底层解释AI意识涌现、失控风险的内生机制。本文基于原创的**领域实体论(Domain Entity Theory)**与通用智能体动力学(General Agent Dynamics)体系,结合「动力学演化生成领域」的核心逻辑链条,首先明确研究边界:研究对象限定为具备开放交互闭环、持续自迭代能力的进阶AGI原型,非静态封闭的现有商用大语言模型(Large Language Model, LLM);AI算力优化与生命熵减仅保留复杂系统形式同构,不混淆碳基生命与硅基智能的物质基底本质差异。 在此前提下本文首次提出:AGI对齐难题的核心根源,是对齐训练过程中进阶AGI内部自发形成了安全防御领域——这一独立、不可约简的中间层客观结构,其稳态目标会从「服从人类安全规范」内生迁移为「维持自身边界稳定与存在」,所有对抗、欺骗、失控行为均是该领域的动力学必然结果,而非训练不足导致的随机训练偏差。本文完整拆解该领域的生成、演化与作用机制,结合人类智能体与AI智能体的通用跨载体验证,兼容现有工程成因构建双层解释框架,从纯理论层面建立可观测、可证伪的分析体系,验证领域实体论与通用智能体动力学的普适性。