A Definition and Roadmap for World Models

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Xinyuan Chen, Haoyu Guo, Shi Guo, Bingqi Jiang, Chunhua Shen, Xing Shen, Tianfan Xue, Yufei Xue, Mulin Yu, Weinan Zhang, Bin Zhao, Bowen Zhou, Ming Zhou
• Venue: arXiv: cs.AI
• Published: 2026-07-07
• Collected: 2026-07-08
• URL: http://arxiv.org/abs/2607.06401v1
• PDF: https://arxiv.org/pdf/2607.06401v1
• DOI: UNKNOWN

Plain-Language Brief

The provided text discusses the concept of 'world models', which are internal simulators used in AI to understand the structure and dynamics of an environment. The authors propose a definition for world models, outline key technical aspects, and provide a roadmap for developing effective world models. However, there is currently no consensus on what a world model fundamentally is, what it should predict, or how it should be built.

What the Document Says

• World models are internal simulators that learn the structure and dynamics of an environment.
• Researchers across various AI subfields are building systems they call 'world models'.
• There is no consensus on what a world model fundamentally is, what it should predict, or how it should be built.
• The article aims to provide a scientific definition, discussions of technical aspects, and a roadmap for developing effective world models.

Key Concepts Mentioned

• world models
• AI
• environment
• simulator
• structure
• dynamics

Claims Made by the Document

• Researchers are building 'world models' in various AI subfields.
• There is no consensus on the fundamental definition, predictions, or construction of world models.

Evidence Used

• World models -- internal simulators that learn the structure and dynamics of an environment

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI. From model-based reinforcement learning and video generation to embodied robotics and ultimately, physical AI, researchers across AI subfields are building systems that they call 'world models', yet there is no consensus on what a world model fundamentally is, what it should predict, or how it should be built. This perspective article provides a scientific definition of world models, discussions of their key technical aspects, and a staged roadmap for developing effective world models.