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.