Toward AI Systems That Understand Self and Others: A Multi-Phase Inference Framework for Human Cognitive Diversity and World-Model Alignment
arXiv:2605.29930v1 Announce Type: new 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 s
The accelerating development of advanced AI models highlights the growing challenge of aligning disparate AI world-models and ensuring robust human-AI interaction, necessitating frameworks that explicitly account for cognitive diversity and inference heterogeneity.
This research is crucial for developing AI systems capable of nuanced understanding and interaction in complex human environments, moving beyond mere data processing to actual cognition and social intelligence.
The focus in AI development shifts from purely performance-driven metrics to incorporating explicit mechanisms for understanding and aligning world models, crucial for multi-agent systems and sophisticated human-AI collaboration.
- · AI researchers (cognitive science)
- · Developers of foundational AI models
- · Human-computer interaction specialists
- · Companies building personalized AI agents
- · Developers of 'one-size-fits-all' AI systems
- · Traditional symbolic AI approaches
This framework could lead to AI systems that are more robust, adaptable, and less prone to 'misunderstanding' human intent or other AI agents.
It might enable the creation of highly specialized, personalized AI agents that deeply understand individual users' cognitive biases and inferential styles.
Such advancements could fundamentally alter human-AI interfaces, leading to more intuitive and collaborative digital assistants and autonomous systems that navigate complex social dynamics.
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Read at arXiv cs.AI