Large World Models: Use Cases & Real-Life Examples ['26]
Despite advances in large language models, artificial intelligence remains limited in its ability to understand and interact with the physical world due to the constraints of text-based representations.
Large world models address this gap by integrating multimodal data to reason about actions, model real-world dynamics, and predict environmental changes.
Discover what large world models are, how they differ from other approaches, their key use cases, real-world examples, and the challenges involved in building them.
What is a large world model?
A large world model (LWM) is an advanced class of artificial intelligence models that go beyond the text-based focus of large language models (LLMs). While LLMs learn patterns from language sequences, LWMs are designed to integrate and process multimodal data across spatial, temporal, and physical dimensions.
These models aim to represent the real world by incorporating text, images, audio, sensor signals, video sequences, and interactive environments.
LWMs are often described as a step closer to building AI systems that can understand and interact with the physical world, offering capabilities such as spatial reasoning, long-term video understanding, and the ability to predict dynamics in complex environments.
Figure 1: An example of a large world model that can answer questions in YouTube videos.1
Architecture of large world models
- Precondition and effect inference: A core feature, informed by recent research, is the explicit modeling of what must be true before an action (precondition) and what changes occur after (effect).2
- Semantic state matching: LWMs utilize modules that align inferred preconditions and effects with current world states, enabling the prediction of valid actions and state transitions.
- Generative models: They generate videos, simulate environments, and predict dynamics in extended video sequences and real-world environments.
- Scalability: Training relies on both real data and unlimited diverse training environments, including synthetic simulations.
Emerging techniques, such as neural radiance fields (NeRFs), Gaussian splatting, and ring attention mechanisms, are utilized to enhance the ability to handle long sequences and dynamic interactions.
How is it different from world foundation models and other world models?
- World foundation models focus on providing a general-purpose backbone for reasoning about the world. Still, they are often closer to the LLM paradigm, emphasizing symbolic and semantic representation of human knowledge.
- World models in reinforcement learning or robotics typically model specific environments for training autonomous agents, often constrained to simulation tools or narrow tasks.
- Large world models: Extend beyond by modeling long sequences of actions, predicting dynamics, and integrating multimodal inputs. LWMs emphasize precondition-effect reasoning, which enables them to answer questions such as “Is this action valid now?” and “What happens if I do this?”, capabilities often lacking in other models.
In short, world foundation models provide a baseline, while LWMs extend these capabilities into physical AI systems and interactive experiences.
Researcher perspectives on large world models
According to research on large world models, it can be inferred to be an internal, general-purpose simulator that uses abstract representations to predict and evaluate future states across open-ended environments.
It is distinct from both small, task-specific world models and large, purely interactive simulations. Its purpose is not to render the world, but to reason about it before acting.
Here are some of the key takeaways:
- First, scale alone is not sufficient. Large environments or complex simulations do not automatically produce large world models, and smaller systems can still qualify as world models when they capture how environments evolve. What matters is the ability to generalize across tasks and domains, not raw size.
- Second, large world models rely on abstraction. Raw sensory detail is often too fragile for general planning, so these models operate on compressed, conceptual representations that preserve what is relevant for reasoning across contexts.
- Third, large world models change the role of language models. Instead of generating only actions or text, language models act as internal simulators that predict how the world might respond to hypothetical actions, enabling deliberation rather than reaction.
- Finally, large world models redefine planning. Planning becomes a process of simulating possible futures, comparing outcomes, and selecting actions based on expected consequences, bringing AI reasoning closer to human decision-making.
PoE-World
PoE-World article3 approaches world models as explicit models of environment dynamics that support planning and control. The article treats a world model as something that predicts how the environment changes in response to actions. Its core concern is not scale, but structure: how to represent the world in a way that supports generalization and long-horizon reasoning.
Instead of relying on a single large neural network, the authors argue that world models should be compositional. They propose building the world model from multiple smaller, programmatic experts, each responsible for a specific factor of the environment, such as object movement or interactions. These experts are combined mathematically to produce overall predictions of future states.
The paper is cautious about large, end-to-end neural world models. It suggests that increasing model size alone does not address issues such as interpretability or systematic reasoning. In their view, structure and modularity matter more than the number of parameters.
Key points
- Defines a world model as a predictor of future observations given past observations and actions.
- Emphasizes compositional and symbolic structure rather than large neural networks.
- Uses multiple small experts combined into a single predictive model.
- Argues that monolithic large world models struggle with long-horizon and compositional reasoning.
- Focuses on planning and control in constrained environments rather than open-ended settings.
LatticeWorld
LatticeWorld4 uses the term world model in a different sense. In this paper, a world model is primarily a large-scale interactive virtual environment rather than a learned predictive model. The focus is on building detailed, explorable 3D worlds for interaction, simulation, and data generation.
The article treats world models as external environments that agents or humans can interact with. These environments include terrain, objects, physics, and multiple agents, and are designed to closely resemble real-world settings to reduce the gap between simulation and reality. The emphasis is on realism and interactivity, not on predicting future states internally.
Large language models play a supporting role. They are used to translate text and visual instructions into symbolic representations that define scene layouts and configurations. The actual world behavior, including physics and interactions, is handled by a game engine rather than by a learned world model.
Key points
- Uses the term “world model” to mean a high-fidelity, interactive simulated environment.
- Focuses on world generation rather than on learning environment dynamics.
- Treats world models as sources of data and interaction rather than reasoning tools.
- Uses LLMs for scene layout and configuration generation, not for prediction or planning.
- Does not model state transitions or counterfactual futures internally.
SIMURA
SIMURA5 places world models at the center of intelligent behavior. It defines a world model as an internal simulator that an agent uses to imagine future states before acting. The paper explicitly contrasts this with token-by-token autoregressive reasoning, which it argues lacks foresight and the ability to perform counterfactual evaluation.
In this framework, the world model predicts how the environment will respond to candidate actions. These predictions are then evaluated against the agent’s goals, enabling it to choose actions based on simulated outcomes rather than immediate responses. The world model is therefore the mechanism that enables planning.
What distinguishes SIMURA is its scale and generality. The world model is implemented using large language models and operates in open-ended environments such as the web. World states are represented in natural language, which allows abstraction and transfer across tasks without retraining separate models for each environment.
Key points
- Defines a world model as an internal simulator used for planning and decision-making.
- Uses world models to evaluate counterfactual futures before acting.
- Implements the world model using large language models.
- Represents world states and transitions in natural language rather than continuous embeddings.
- Targets general, open-ended environments rather than narrow tasks.
Use cases of large world models
Healthcare
LWMs in healthcare can integrate patient records, genomic data, and real-time biometrics with environmental inputs. By modeling interactions across these datasets, they can support personalized treatments, predict health risks earlier, and guide surgical decision-making with real-time analysis.
Urban planning and smart cities
By analyzing traffic flows, energy consumption, and environmental data, LWMs can simulate city-scale interventions. For example, they can predict how new infrastructure projects impact pollution, mobility, or energy demand, enabling informed decisions in complex environments.
Robotics and autonomous systems
For autonomous vehicles and robots, LWMs provide a deeper understanding of spatial properties and object interactions. They support training in diverse training environments and real-world conditions, allowing autonomous machines to navigate more safely and adaptively.
Education and training
LWMs can generate interactive experiences and realistic virtual worlds for skill training. In fields such as aviation or medicine, LWMs can simulate high-risk scenarios, enabling learners to practice within safe yet realistic virtual environments.
Environmental monitoring
LWMs process satellite data, sensor feeds, and extended sequences of environmental information to predict climate dynamics. This enables stakeholders to optimize resource utilization, track the impacts of deforestation, or model disaster scenarios.
Gaming and entertainment
With the ability to generate videos and immersive simulations from a single prompt image or language description, LWMs open possibilities for interactive experiences in gaming, AR, and VR. Their ability to create million-length video sequences offers a leap in realism and creativity.
Real-life examples of large world models
Genie 2: A foundation world model for diverse 3D environments
Google DeepMind introduced Genie 2, a large-scale foundation model for generating interactive 3D environments from a single prompt image. These environments can be controlled by humans or AI agents using standard input devices, such as keyboards and mice. The model addresses a long-standing challenge in AI research: the lack of sufficiently varied training environments for embodied agents.
Key capabilities:
- Environment generation: Produces diverse 3D worlds from images created by text-to-image systems.
- Action controllability: Simulates the effects of actions such as movement, jumping, or object interactions.
- Counterfactual simulation: Enables alternative trajectories from the same starting point, supporting experimentation.
- Long-horizon memory: Retains information about previously unseen parts of the environment and accurately restores them when revisited.
- Physical realism: Models physics-related effects such as gravity, water, smoke, and lighting.
- Complex scenes: Handles character animation, NPCs, and multi-agent interactions in generated worlds.
Applications:
- Training and evaluation of AI agents: Genie 2 offers novel environments for testing generalization and adaptability.
- Rapid prototyping: Researchers, artists, and designers can quickly transform concept art into interactive spaces, thereby accelerating their creative workflows.
- Interactive experiences: Supports both human exploration and AI testing in varied scenarios, including first-person, third-person, and isometric perspectives.
Architecture:
Genie 2 is an autoregressive latent diffusion model, trained on large-scale video data. It utilizes a transformer dynamics model, similar to those used in large language models, but adapted for sequential video frames. Actions and past frames are processed autoregressively, enabling frame-by-frame simulation.
Figure 2: The example model architecture for Genie 2.6
Decart
Decart’s work on large world models (LWMs) spans both consumer experiences and enterprise infrastructure.
Its Oasis platform enables users to generate and explore adaptive virtual worlds with real-time video and interactive features that evolve in response to user input. Often compared to Minecraft, Oasis has drawn millions of users for its dynamic audio-visual experiences.
For enterprises, Decart provides a GPU optimization tool that improves efficiency during training and inference. This solution accelerates model development, reduces deployment costs, and enables companies to scale AI applications more affordably.7
World Labs
World Labs, founded by Fei-Fei Li with a team of leading AI researchers, is building large world models that focus on spatial intelligence. The company has attracted significant investment and quickly reached unicorn status, reflecting the growing interest in interactive 3D world generation.
Its technology can take a single image and transform it into an interactive 3D environment with depth, physics, and spatial consistency. Unlike earlier 2D-to-3D approaches, these scenes maintain coherence and allow real-time exploration directly in a browser.
World Labs is targeting creators in fields such as gaming, film, design, architecture, robotics, and engineering, offering tools for rapid, controllable, and high-fidelity 3D world creation.8
Challenges and how to mitigate them
Despite their promise, LWMs face several challenges:
- Data complexity: Training requires massive, multimodal datasets that cover video, audio, sensor, and language sequences. Mitigation involves combining synthetic data generation with fine-tuning on real-world data.
- Compute intensity: Handling long sequences and video understanding demands extensive computational power. Techniques like ring attention and optimized sequence lengths are being developed to make training more efficient.
- Bias and safety: Incorporating human knowledge and real-world data raises risks of bias or misuse. Careful model training, evaluation on new benchmarks, and ethical oversight are essential.
- Privacy: Real-world environments often include personal and sensitive information. Privacy-preserving training and clear governance frameworks are necessary.
Future outlook
Large world models represent a paradigm shift in artificial intelligence. They are not just larger versions of existing models but introduce the capacity to learn from real-world environments, generate physics-aware videos, and enable autonomous machines to act in dynamic settings.
As the technology matures, LWMs are likely to form the backbone of physical AI systems that bridge virtual and real-world experiences, supporting both specialized industrial applications and consumer-facing interactive experiences.
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