The Trinity of Consistency as a Defining Principle for General World Models

• URL: http://arxiv.org/abs/2602.23152v1
• Date: Thu, 26 Feb 2026 16:15:55 GMT
• Title: The Trinity of Consistency as a Defining Principle for General World Models
• Abstract summary: General World Models are capable of learning, simulating, and reasoning about objective physical laws.<n>We propose a principled theoretical framework that defines the essential properties requisite for a General World Model.<n>Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.
• Score: 106.16462830681452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The construction of World Models capable of learning, simulating, and reasoning about objective physical laws constitutes a foundational challenge in the pursuit of Artificial General Intelligence. Recent advancements represented by video generation models like Sora have demonstrated the potential of data-driven scaling laws to approximate physical dynamics, while the emerging Unified Multimodal Model (UMM) offers a promising architectural paradigm for integrating perception, language, and reasoning. Despite these advances, the field still lacks a principled theoretical framework that defines the essential properties requisite for a General World Model. In this paper, we propose that a World Model must be grounded in the Trinity of Consistency: Modal Consistency as the semantic interface, Spatial Consistency as the geometric basis, and Temporal Consistency as the causal engine. Through this tripartite lens, we systematically review the evolution of multimodal learning, revealing a trajectory from loosely coupled specialized modules toward unified architectures that enable the synergistic emergence of internal world simulators. To complement this conceptual framework, we introduce CoW-Bench, a benchmark centered on multi-frame reasoning and generation scenarios. CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol. Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.

Related papers

• Platonic Representation Hypothesis on World Models [42.05154017234555]
  We show that capable world models evolve toward geometrically similar internal structures.<n>Our findings suggest that the pursuit of predictive consistency can promote shared, transition-compatible latent structure across world models.
  arXiv  Detail & Related papers  (2026-08-24T18:08:30Z)
• WorldBagel: Uncovering the Power of Unified Multimodal Models for Vision-Language-Action-World Modeling [53.14409745466386]
  World models aim to capture environment dynamics in ways that support perception, reasoning, and action.<n>We introduce textttWorldBagel, a unified VLAW framework built on BAGEL, a modern multimodal unified model.<n>We show that textttWorldBagel consistently outperforms task-specific alternatives and learns action representations that are more structured and semantically aligned with visual and linguistic context.
  arXiv  Detail & Related papers  (2026-07-03T16:18:20Z)
• A Tutorial on World Models and Physical AI [0.30458514384586394]
  World modeling is emerging as a central principle for building intelligent systems capable of prediction, reasoning, and decision making.<n>This tutorial presents a coherent framework in which diverse world modeling approaches are unified through shared predictive structure.
  arXiv  Detail & Related papers  (2026-06-11T00:52:39Z)
• World Action Models: The Next Frontier in Embodied AI [123.5787299299832]
  Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning.<n>They learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention.<n>A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline.
  arXiv  Detail & Related papers  (2026-05-12T13:10:52Z)
• HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation [83.31948299340782]
  HERMES++ is a unified driving world model that integrates 3D scene understanding and future geometry prediction within a single framework.<n>Hermes++ achieves strong performance, outperforming specialist approaches in both future point cloud prediction and 3D scene understanding tasks.
  arXiv  Detail & Related papers  (2026-04-30T17:59:58Z)
• UniG2U-Bench: Do Unified Models Advance Multimodal Understanding? [50.92401586025528]
  Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear.<n>We introduce UniG2U-Bench, a comprehensive benchmark categorizing generation-to-understanding (G2U) evaluation into 7 regimes and 30 subtasks.
  arXiv  Detail & Related papers  (2026-03-03T18:36:16Z)
• 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)
• A Mechanistic View on Video Generation as World Models: State and Dynamics [43.951972667861575]
  This work proposes a novel taxonomy centered on two pillars: State Construction and Dynamics Modeling.<n>By addressing these challenges, the field can evolve from generating visually plausible videos to building robust, general-purpose world simulators.
  arXiv  Detail & Related papers  (2026-01-22T19:00:18Z)
• Natural Building Blocks for Structured World Models: Theory, Evidence, and Scaling [42.78591555984395]
  We propose a framework that specifies the natural building blocks for structured world models.<n>We examine Hidden Markov Models (HMMs) and linear switching dynamical systems (sLDS) as natural building blocks for discrete and continuous modeling.<n>This modular approach supports both passive modeling (generation, forecasting) and active control (planning, decision-making) within the same architecture.
  arXiv  Detail & Related papers  (2025-11-03T22:02:04Z)
• Co-Evolving Latent Action World Models [57.48921576959243]
  Adapting pre-trained video models into controllable world models via latent actions is a promising step towards creating generalist world models.<n>We propose CoLA-World, which for the first time successfully realizes this synergistic paradigm.<n>This unlocks a co-evolution cycle: the world model acts as a knowledgeable tutor, providing gradients to shape a high-quality LAM.
  arXiv  Detail & Related papers  (2025-10-30T12:28:40Z)
• World Models Should Prioritize the Unification of Physical and Social Dynamics [57.91940497010114]
  This paper argues that the systematic, bidirectional unification of physical and social predictive capabilities is the next crucial frontier for world model development.<n>We contend that comprehensive world models must holistically integrate objective physical laws with the subjective, evolving, and context-dependent nature of social dynamics.
  arXiv  Detail & Related papers  (2025-10-24T07:42:37Z)
• 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)
• Learning Local Causal World Models with State Space Models and Attention [1.5498250598583487]
  We show that a SSM can model the dynamics of a simple environment and learn a causal model at the same time.<n>We pave the way for further experiments that lean into the strength of SSMs and further enhance them with causal awareness.
  arXiv  Detail & Related papers  (2025-05-04T11:57:02Z)
• Aether: Geometric-Aware Unified World Modeling [49.33579903601599]
  Aether is a unified framework that enables geometry-aware reasoning in world models.<n>Our framework achieves zero-shot generalization in both action following and reconstruction tasks.<n>We hope our work inspires the community to explore new frontiers in physically-reasonable world modeling.
  arXiv  Detail & Related papers  (2025-03-24T17:59:51Z)

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.