Mutual misunderstanding in contemporary society does not arise merely because people hold different opinions or values. Even under the same observations, different subjects may form different inferential targets, state representations, prediction errors, and update priorities. This paper proposes a multi-phase inference framework and defines its core internal mechanism as the Multi-Phase Inference Mechanism (MIM). MIM formalizes how heterogeneous world models arise through a phase-formation space, a foregrounding field, subject-specific profile states, and alignment maps between state representations. On this basis, the paper reframes world-model alignment as the problem of making heterogeneous representations mutually processable, rather than forcing agreement or convergence to a single value system. It further connects this formalism to philosophical disagreements, cognitive typology, social fragmentation, and AI alignment. The aim is to provide a constructive vocabulary for AI systems that can help humans understand self and others by making differences in meaning, value, and prediction error visible, comparable, and transformable.

翻译：当代社会中的相互误解不仅源于人们持有不同的观点或价值观。即便面对相同的观察结果，不同主体也可能形成差异化的推理目标、状态表征、预测误差和更新优先级。本文提出一种多阶段推理框架，并将其核心内部机制定义为多阶段推理机制（MIM）。MIM通过阶段形成空间、前景化场域、主体特定轮廓状态及状态表征间的对齐映射，形式化描述了异质性世界模型的产生过程。在此基础上，本文将世界模型对齐重新定义为使异质性表征能够相互可处理的问题，而非强制达成共识或趋同于单一价值体系。进一步地，本文将这一形式化框架与哲学分歧、认知类型学、社会分裂及AI对齐问题建立关联。研究旨在为能够帮助人类理解自我与他者的AI系统提供建设性术语体系，使意义、价值与预测误差的差异得以可视化、可比较与可转化。