🤖 AI Summary
This work addresses how cognitive differences among humans lead to heterogeneous reasoning objectives, state representations, and prediction errors from identical observations, thereby causing mutual misunderstanding. The paper proposes a Multi-stage Inference Model (MIM) that reframes the world model alignment problem as one of interoperability among heterogeneous representations rather than enforced convergence. By introducing a phased inference mechanism—integrating phase-generated representational spaces, agent-specific state encodings, and alignment mappings—the framework establishes a theoretical foundation grounded in cognitive typology and formalized philosophical divergence. This approach enables visualization, comparability, and transformability of cognitive differences, offering a novel pathway toward understanding human cognitive diversity, mitigating societal cognitive fragmentation, and advancing human-AI value alignment.
📝 Abstract
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.