Toward AI That Understands Self and Others: A World-Model Theory of Cognitive Diversity and Alignment
Toru Takahashi
Why It Matters
What makes this one worth your time
This research could inform the design of AI systems that respect diverse cognitive perspectives while maintaining effective communication and error detection.
A new framework for understanding cognitive diversity in AI systems.
Summary
The paper proposes a world-model theory that addresses cognitive diversity and alignment by introducing the Multi-Phase Inference Assumption and the Multi-Phase Inference Mechanism, focusing on how heterogeneous world models can communicate without converging into a single representation.
Key contributions
- Development of the Multi-Phase Inference Assumption (MIA).
- Introduction of the Multi-Phase Inference Mechanism (MIM).
- Framework for analyzing communication between heterogeneous world models.
Notable insights
- The distinction between observation and inference highlights the complexity of cognitive processes in AI.
- The introduction of alignment maps and transformation loss offers a novel approach to managing cognitive diversity.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.29930v2 Announce Type: replace Abstract: 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.