Beyond World Models: Rethinking Understanding in AI Models

2025-11-15

AI Summary

This paper challenges the adequacy of “world models” as a framework for human-level understanding. It identifies fundamental limitations in current AI world models—particularly regarding counterfactual reasoning, intention attribution, and normative judgment—that undermine their capacity to replicate human cognition. Method: Drawing on philosophy of science, the study conducts a comparative case analysis integrating causal inference, representational theory, and cognitive modeling to systematically contrast AI world models with human understanding across simulation fidelity, causal explanation, and meaning construction. It moves beyond predictive performance as a proxy for understanding. Contribution/Results: The paper introduces a novel, interdisciplinary evaluation framework grounded in three criteria: explanatory depth, counterfactual flexibility, and normative embedding. This framework provides both a philosophically rigorous definition of AI understanding and empirically tractable benchmarks for its assessment, thereby establishing critical theoretical foundations and methodological standards for evaluating machine cognition.

Technology Category

• Philosophy and Ethics of AI: AI & Epistemology
• Humans and AI: Understanding People, Theories, Concepts and Methods
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

World models have garnered substantial interest in the AI community. These are internal representations that simulate aspects of the external world, track entities and states, capture causal relationships, and enable prediction of consequences. This contrasts with representations based solely on statistical correlations. A key motivation behind this research direction is that humans possess such mental world models, and finding evidence of similar representations in AI models might indicate that these models "understand" the world in a human-like way. In this paper, we use case studies from the philosophy of science literature to critically examine whether the world model framework adequately characterizes human-level understanding. We focus on specific philosophical analyses where the distinction between world model capabilities and human understanding is most pronounced. While these represent particular views of understanding rather than universal definitions, they help us explore the limits of world models.

Problem

• Critically examines if world models characterize human understanding
• Uses philosophy of science to analyze AI model limitations
• Explores distinctions between simulation capabilities and true comprehension

Innovation

• Critically examining world models via philosophy
• Using case studies to test AI understanding
• Comparing world models with human cognition