Modern societies possess more information than ever before, yet they do not converge toward a single shared understanding. The same events, facts, laws, technologies, or risks can be interpreted as evidence of freedom, danger, exclusion, injustice, responsibility, or unrealized possibility. Existing discussions often treat such disagreement as a conflict of values, preferences, or beliefs. This paper argues that disagreement is already a late-stage phenomenon. The central premise is simple but not trivial: observation is not yet inference. Not every observation becomes inferentially relevant, and not every possible object in an observation sequence becomes an estimation target. A possible target becomes admissible only when a state representation can be constructed that is approximately sufficient for prediction, evaluation, or action with respect to that target. This paper develops a world-model theory of cognitive diversity and alignment by reconstructing recognition as the construction of such approximate sufficient statistics under finite informational, representational, observational, and action constraints. It formulates this position as the Multi-Phase Inference Assumption (MIA) and defines its core internal mechanism as the Multi-Phase Inference Mechanism (MIM). The framework introduces alignment maps and transformation loss to analyze how heterogeneous world models communicate without being collapsed into a single representation. World-model alignment is therefore processability, not agreement: the design of AI systems that help heterogeneous forms of intelligence remain mutually processable while preserving their distinct error-detection capacities.

翻译：现代社会拥有比以往更多的信息，却并未趋同于单一共享的理解。相同的事件、事实、法律、技术或风险，可以被解读为自由的证据、危险的信号、排斥的根源、不公的体现、责任的承担，或是未实现的可能。现有讨论常将此类分歧视为价值观、偏好或信念的冲突。本文认为，分歧已是晚发阶段的现象。核心前提简单却非琐碎：观察尚非推理。并非所有观察都具有推理相关性，也并非观察序列中的每个可能对象都会成为估计目标。仅当能够构建出对该目标而言近似充分的预测、评估或行动的状态表征时，该潜在目标才成为可容许对象。本文通过将识别重构为在有限信息、表征、观察和行动约束下构建此类近似充分统计量的过程，发展出一套认知多样性与对齐的世界模型理论。我们将此立场形式化为多阶段推理假设（MIA），并将其核心内部机制定义为多阶段推理机制（MIM）。该框架引入对齐映射与变换损失，以分析异质性世界模型如何在无需坍缩为单一表征的情况下进行交流。因此，世界模型对齐并非达成一致，而是可处理性：设计能够使异质智能形式在保持彼此可处理的同时，保留其各自错误检测能力的人工智能系统。