- Current AI systems often lack consistency: objects deform, spaces change, and time gets “broken” in videos or simulations.
- The core cause is that generative AI operates on a mechanism of probabilistic prediction, without maintaining a continuous world model to update its understanding.
- World models are proposed as a solution, helping AI build and update spatiotemporal maps (4D: 3D + time).
- For example, current video AI does not “remember” a dog’s chair or collar because it lacks a stable scene model.
- New research shows that 4D world models help AI maintain object and motion consistency.
- Techniques like NeRF (from 2020) allow 3D scene reconstruction from multiple angles but remain data-dependent.
- New studies like NeoVerse and TeleWorld convert single videos into 4D models to generate video from multiple angles.
- World models serve not only video but are also crucial for AR, robotics, and autonomous vehicles.
- With AR, a world model helps virtual objects stay stationary, occlude correctly, and have logical lighting and perspective.
- Robots and autonomous vehicles can use world models to predict the environment’s next developments.
- 2025 benchmarks show that current vision-language AI is nearly random when distinguishing motion trajectories.
- LLMs like ChatGPT have an “implicit understanding” of the world but cannot update in real-time.
- OpenAI admits that GPT-4 does not learn from post-deployment experience.
- Many researchers believe AGI cannot be achieved without world models possessing spatiotemporal memory.
- World models are viewed as the foundational layer, while LLMs play the role of communication and linguistic reasoning.
- Big names are shifting to world models: Fei-Fei Li founded World Labs (2024), Yann LeCun founded AMI Labs (2025).
- Research on DreamerV3 (Nature, April 2025) shows that AI with a world model can “imagine” the future to improve behavior.
- 4D world models also serve as safe simulation environments to test AI before real-world deployment.
📌 Current AI systems often lack consistency: objects deform, spaces change, and time gets “broken” in videos or simulations. The core cause is that generative AI operates on a mechanism of probabilistic prediction, without maintaining a continuous world model to update its understanding. World models are emerging as the foundation for the next wave of AI, solving AI’s biggest current weakness: a lack of stable understanding of space and time. From video, AR, and robotics to AGI, the ability to build and update continuous world models could determine whether AI merely “mimics” or truly understands and acts correctly in the real world.
