Beyond World Models: Rethinking Understanding in AI Models

• URL: http://arxiv.org/abs/2511.12239v1
• Date: Sat, 15 Nov 2025 14:45:26 GMT
• Title: Beyond World Models: Rethinking Understanding in AI Models
• Authors: Tarun Gupta, Danish Pruthi,
• Abstract summary: World models are internal representations that simulate aspects of the external world.<n>Finding evidence of similar representations in AI models might indicate that these models "understand" the world in a human-like way.<n>This paper critically examines whether the world model framework adequately characterizes human-level understanding.
• Score: 15.246406031450775
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks [43.59401259468559]
  We argue that a robust world model should not be a loose collection of capabilities but a normative framework that integrally incorporates interaction, perception, symbolic reasoning, and spatial representation.<n>This work aims to guide future research toward more general, robust, and principled models of the world.
  arXiv  Detail & Related papers  (2026-02-02T04:42:44Z)
• Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models [60.543714835980325]
  Humans construct internal world models and reason by manipulating the concepts within these models.<n>Recent advances in AI approximate such human cognitive abilities, where world models are believed to be embedded within large language models.<n>This paper presents the first principled study of when and how visual generation benefits reasoning.
  arXiv  Detail & Related papers  (2026-01-27T17:40:07Z)
• The Universal Landscape of Human Reasoning [60.72403709545137]
  We introduce Information Flow Tracking (IF-Track) to quantify information entropy and gain at each reasoning step.<n>We show that IF-Track captures essential reasoning features, identifies systematic error patterns, and characterizes individual differences.<n>This approach establishes a quantitative bridge between theory and measurement, offering mechanistic insights into the architecture of reasoning.
  arXiv  Detail & Related papers  (2025-10-24T16:26:36Z)
• WoW: Towards a World omniscient World model Through Embodied Interaction [83.43543124512719]
  Authentic physical intuition of the world model must be grounded in extensive, causally rich interactions with the real world.<n>We present WoW, a generative world model trained on 2 million robot interaction trajectories.<n>We establish WoWBench, a new benchmark focused on physical consistency and causal reasoning in video.
  arXiv  Detail & Related papers  (2025-09-26T17:59:07Z)
• Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models [93.1043186636177]
  We explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations.<n>We propose a computational implementation of this idea -- a Model Synthesis Architecture''<n>We evaluate our MSA as a model of human judgments on a novel reasoning dataset.
  arXiv  Detail & Related papers  (2025-07-16T18:01:03Z)
• Understanding World or Predicting Future? A Comprehensive Survey of World Models [21.96900555014452]
  This survey offers a comprehensive review of the literature on world models.<n>World models are regarded as tools for either understanding the present state of the world or predicting its future dynamics.
  arXiv  Detail & Related papers  (2024-11-21T03:58:50Z)
• Elements of World Knowledge (EWoK): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Models [51.891804790725686]
  Elements of World Knowledge (EWoK) is a framework for evaluating language models' understanding of conceptual knowledge underlying world modeling.<n>EWoK-core-1.0 is a dataset of 4,374 items covering 11 world knowledge domains.<n>All tested models perform worse than humans, with results varying drastically across domains.
  arXiv  Detail & Related papers  (2024-05-15T17:19:42Z)
• The Essential Role of Causality in Foundation World Models for Embodied AI [102.75402420915965]
  Embodied AI agents will require the ability to perform new tasks in many different real-world environments. Current foundation models fail to accurately model physical interactions and are therefore insufficient for Embodied AI. The study of causality lends itself to the construction of veridical world models.
  arXiv  Detail & Related papers  (2024-02-06T17:15:33Z)
• Causal World Models by Unsupervised Deconfounding of Physical Dynamics [20.447000858907646]
  The capability of imagining internally with a mental model of the world is vitally important for human cognition. We propose Causal World Models (CWMs) that allow unsupervised modeling of relationships between the intervened and alternative futures. We show reductions in complexity sample for reinforcement learning tasks and improvements in counterfactual physical reasoning.
  arXiv  Detail & Related papers  (2020-12-28T13:44:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.