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

arXiv: https://arxiv.org/abs/2602.23152 · HTML: https://mlsanity.com/paper/2602.23152/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 0 of its 455 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Built on by (most cited first, 6 of 6)

- [A Definition and Roadmap for World Models](https://mlsanity.com/paper/2607.06401/) (2026)
- [OpenWorldLib: A Unified Codebase and Definition of Advanced World Models](https://mlsanity.com/paper/2604.04707/) (2026)
- [PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams](https://mlsanity.com/paper/2606.07454/) (2026)
- [Bridging Modal Isolation in Interleaved Thinking: Supervising Modality Transitions via Stepwise Reinforcement](https://mlsanity.com/paper/2606.12886/) (2026)
- [Persistent Computational State: A Session-Centric Runtime for Generative World Models](https://mlsanity.com/paper/2607.21686/) (2026)
- [Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation](https://mlsanity.com/paper/2610.01092/) (2026)

## Abstract (arXiv)

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.