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README.md

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+# 🌐 World Model
+
+> **Architectures, components and systems for building predictive world models.**
+
+**World Model** is a Hugging Face community focused on the **technical architecture of world-model systems**: how they represent environments, learn dynamics, predict future states, simulate alternatives, support planning and connect perception with action.
+
+Where the broader [`world-models`](https://huggingface.co/world-models) organization maps the **ecosystem**, this organization focuses on a more specific question:
+
+> **How does a World Model actually work?**
+
+The goal is to make world-model architectures understandable, inspectable and practically useful for developers, researchers, students, agent builders, robotics engineers and anyone working on predictive AI systems.
+
+---
+
+# Explore the World Model Engineering Stack
+
+The organization now provides a practical four-part engineering stack:
+
+```text
+ARCHITECTURE
+     ↓
+IMPLEMENTATION
+     ↓
+TRAINING & EVALUATION
+     ↓
+DATA & BENCHMARKS
+```
+
+### 🧱 [World Model Architecture Explorer](https://huggingface.co/spaces/world-model/world-model-architecture-explorer)
+
+Explore major architecture patterns and system components:
+
+- latent dynamics
+- encoder–dynamics–decoder systems
+- action-conditioned models
+- world model + planner
+- world model + policy
+- generative and interactive world models
+- world action models
+- multimodal architectures
+- memory-augmented architectures
+- object-centric and hierarchical world models
+
+### 🧩 [World Model Implementation Lab](https://huggingface.co/spaces/world-model/world-model-implementation-lab)
+
+Move from diagrams to concrete implementations.
+
+The repository includes compact PyTorch examples for:
+
+- latent-state encoding
+- learned dynamics
+- action conditioning
+- future-state prediction
+- imagined rollouts
+- reward prediction
+- candidate-action planning
+- Model Predictive Control
+- end-to-end toy world-model training
+
+### 🧪 [World Model Training & Evaluation Lab](https://huggingface.co/spaces/world-model/world-model-training-evaluation-lab)
+
+Study advanced methods for making world models useful beyond one-step prediction:
+
+- multi-step prediction loss
+- latent overshooting
+- scheduled sampling
+- action-conditioned consistency
+- rollout-error analysis
+- uncertainty estimation
+- calibration
+- distribution shift
+- planning utility
+- efficiency evaluation
+
+### 🧬 [World Model Data & Benchmark Lab](https://huggingface.co/spaces/world-model/world-model-data-benchmark-lab)
+
+Explore the data and benchmark layer:
+
+- transition datasets
+- episodic sequence formats
+- multimodal trajectories
+- train / validation / test split strategies
+- temporal leakage prevention
+- multi-horizon benchmarks
+- action fidelity
+- OOD generalization
+- planning utility
+- control benchmarks
+- efficiency metrics
+
+These Spaces are designed to complement each other rather than duplicate the same material.
+
+---
+
+# Curated Collections
+
+The organization maintains three curated Hugging Face Collections that connect the practical Spaces with relevant models, datasets, papers and technical resources.
+
+## World Model — Architectures, Training and Implementation
+
+The main technical collection.
+
+It connects:
+
+- the four `world-model` Spaces
+- reference world-model implementations
+- modern architecture families
+- training datasets
+- foundational papers
+- planning and control systems
+
+Use this collection as the broad technical entry point to the organization.
+
+## World Model Architectures & Components
+
+A deeper architecture-focused collection covering:
+
+- latent dynamics
+- predictive representations
+- action conditioning
+- JEPA-style world models
+- generative dynamics
+- interactive world models
+- planning
+- world action models
+- structured and multimodal architectures
+
+Use this collection when the question is:
+
+> **How should a world model be structured?**
+
+## World Model Training, Data & Evaluation
+
+A focused collection for:
+
+- training datasets
+- action-conditioned trajectories
+- long-horizon prediction
+- benchmark design
+- robotics evaluation
+- agent-world evaluation
+- Physical AI
+- distribution shift
+- planning utility
+- control performance
+
+Use this collection when the question is:
+
+> **How should a world model be trained and how do we know whether it actually works?**
+
+➡️ [Browse the World Model Collections](https://huggingface.co/world-model/collections)
+
+---
+
+
+## What is a World Model?
+
+A **world model** is an AI system that learns an internal representation of an environment and aspects of how that environment changes over time.
+
+A world model can help answer questions such as:
+
+- What is happening now?
+- What state is the environment in?
+- What is likely to happen next?
+- What happens if an action is taken?
+- Which future states are possible?
+- Which actions may lead toward a goal?
+- How uncertain is the prediction?
+- Can the system simulate multiple possible futures?
+
+In its simplest form:
+
+```text
+Current State + Action
+          ↓
+      World Model
+          ↓
+Predicted Future State
+```
+
+The key idea is that intelligence often requires more than reacting to the present.
+
+A system that can build an internal model of its environment can potentially **predict, simulate, compare, plan and act**.
+
+---
+
+# Anatomy of a World Model
+
+A practical world-model system often contains several functional layers.
+
+```text
+┌────────────────────────────────────────────┐
+│                OBSERVATIONS                │
+│ image · video · language · sensors · state │
+└─────────────────────┬──────────────────────┘
+                      ↓
+┌────────────────────────────────────────────┐
+│                  ENCODER                   │
+│ transforms observations into useful state │
+└─────────────────────┬──────────────────────┘
+                      ↓
+┌────────────────────────────────────────────┐
+│            INTERNAL / LATENT STATE         │
+│ compact representation of the environment │
+└─────────────────────┬──────────────────────┘
+                      ↓
+┌────────────────────────────────────────────┐
+│              DYNAMICS MODEL                │
+│ learns how states evolve through time      │
+└─────────────────────┬──────────────────────┘
+                      ↓
+┌────────────────────────────────────────────┐
+│             FUTURE STATE MODEL             │
+│ predicts possible next states / rollouts   │
+└─────────────────────┬──────────────────────┘
+                      ↓
+┌────────────────────────────────────────────┐
+│          PLANNER / POLICY / CONTROLLER     │
+│ evaluates futures and selects actions      │
+└─────────────────────┬──────────────────────┘
+                      ↓
+┌────────────────────────────────────────────┐
+│                   ACTION                   │
+│ robot · agent · software · control signal  │
+└─────────────────────┬──────────────────────┘
+                      ↓
+             NEW OBSERVATION
+```
+
+Not every world model contains every block.
+
+Some systems focus only on prediction. Others combine perception, dynamics, action generation and planning into one end-to-end architecture.
+
+The core loop is:
+
+> **Observe → Represent → Predict → Simulate → Plan → Act**
+
+---
+
+# 1. Observations
+
+A world model begins with information about an environment.
+
+Possible inputs include:
+
+- images
+- video
+- text
+- audio
+- depth
+- point clouds
+- robot state
+- joint positions
+- proprioception
+- actions
+- rewards
+- maps
+- GPS
+- IMU data
+- API responses
+- software state
+- game state
+- multimodal sensor streams
+
+A digital agent may observe websites, files, messages, APIs and application state. A robot may observe RGB video, depth, joint state, force sensors and language commands.
+
+The observation space defines what the system can perceive.
+
+---
+
+# 2. Encoders
+
+The **encoder** maps raw observations into an internal representation.
+
+```text
+Raw Observation
+      ↓
+    Encoder
+      ↓
+ Internal State
+```
+
+The encoder may be:
+
+- a convolutional neural network
+- a Vision Transformer
+- a language model
+- a multimodal transformer
+- a variational encoder
+- a tokenizer
+- a state estimator
+- a learned feature extractor
+
+Its job is not merely compression. It should preserve information that matters for prediction, control and planning.
+
+---
+
+# 3. Latent State
+
+Many world models operate in a **latent space** rather than directly in pixels or sensor values.
+
+```text
+Observation
+    ↓
+ Encoder
+    ↓
+Latent State zₜ
+```
+
+A useful latent state may encode:
+
+- object relationships
+- motion
+- geometry
+- semantic state
+- hidden variables
+- uncertainty
+- agent intent
+- environmental dynamics
+
+Latent-state modeling can make prediction and planning far more efficient than modeling every raw detail.
+
+---
+
+# 4. Dynamics Model
+
+The **dynamics model** is one of the defining components of a world model.
+
+It learns how the environment changes.
+
+```text
+Stateₜ + Actionₜ
+        ↓
+ Dynamics Model
+        ↓
+Stateₜ₊₁
+```
+
+The dynamics model may learn:
+
+- deterministic transitions
+- stochastic transitions
+- latent transitions
+- object motion
+- reward dynamics
+- physical interactions
+- agent-environment interactions
+- temporal dependencies
+
+Possible implementations include recurrent neural networks, transformers, state-space models, diffusion models, autoregressive models, graph neural networks and hybrid architectures.
+
+---
+
+# 5. Action Conditioning
+
+A world model becomes especially useful when its predictions depend on actions.
+
+Without action conditioning:
+
+```text
+Current State
+     ↓
+ World Model
+     ↓
+Possible Next State
+```
+
+With action conditioning:
+
+```text
+Current State + Action
+          ↓
+      World Model
+          ↓
+Action-Dependent Future State
+```
+
+This is fundamental for robotics, autonomous systems, reinforcement learning, interactive environments and AI agents.
+
+An action-conditioned world model can ask:
+
+> **What will happen if I do this?**
+
+---
+
+# 6. Future-State Prediction
+
+A world model may predict:
+
+- one next state
+- multiple next states
+- trajectories
+- future observations
+- future latent states
+- rewards
+- actions
+- object positions
+- videos
+- maps
+- agent outcomes
+
+World models can be deterministic, stochastic or multi-hypothesis.
+
+```text
+Current State
+     ├── Future A
+     ├── Future B
+     └── Future C
+```
+
+Real environments are uncertain, so representing multiple plausible futures can be important.
+
+---
+
+# 7. Rollouts
+
+A **rollout** repeatedly applies the model to simulate a longer trajectory.
+
+```text
+State₀
+  ↓
+State₁
+  ↓
+State₂
+  ↓
+State₃
+```
+
+With actions:
+
+```text
+State₀ + Action₀
+        ↓
+State₁ + Action₁
+        ↓
+State₂ + Action₂
+        ↓
+State₃
+```
+
+Rollouts support planning, control, reinforcement learning, agent reasoning and scenario analysis.
+
+A major challenge is **error accumulation**: small mistakes can compound over many predicted steps.
+
+---
+
+# 8. Planning
+
+World models become particularly powerful when combined with a planner.
+
+```text
+Current State
+     │
+     ├── Action A → Future A
+     ├── Action B → Future B
+     └── Action C → Future C
+```
+
+The planner can compare futures and select an action.
+
+```text
+World Model
+    ↓
+Possible Futures
+    ↓
+Evaluation
+    ↓
+Best Candidate Action
+```
+
+Planning methods may include:
+
+- Model Predictive Control
+- tree search
+- trajectory optimization
+- sampling
+- beam search
+- policy search
+- value functions
+- learned planners
+- LLM-based reasoning
+- hybrid symbolic-neural planning
+
+World models do not replace planners. They provide the **predictive substrate** on which planning can operate.
+
+---
+
+# 9. Policies
+
+In reinforcement learning and control, a **policy** maps states to actions.
+
+```text
+State → Policy → Action
+```
+
+A world model can provide imagined experience for policy learning:
+
+```text
+World Model
+    ↓
+Imagined Trajectories
+    ↓
+Policy Learning
+```
+
+This is one of the central ideas of model-based reinforcement learning.
+
+---
+
+# 10. Reward and Value Models
+
+Some world-model systems include reward prediction, cost prediction, value estimation, success prediction or safety constraints.
+
+```text
+Predicted Future
+      ↓
+Reward / Value Model
+      ↓
+Evaluation
+```
+
+This lets a system estimate which future is desirable, risky, efficient or closer to a goal.
+
+---
+
+# 11. Memory
+
+World models may need memory because environments are often only partially observable.
+
+Memory can take the form of:
+
+- recurrent state
+- attention over history
+- external memory
+- latent memory
+- episodic memory
+- scene graphs
+- maps
+- key-value stores
+
+Memory is especially important for long-horizon agents, navigation, robotics and persistent environments.
+
+---
+
+# 12. Multimodal World Models
+
+Real environments are multimodal.
+
+A world model may combine:
+
+```text
+      Vision ───┐
+    Language ───┤
+       Audio ───┤
+       Depth ───┼──► WORLD STATE
+     Sensors ───┤
+     Actions ───┤
+      Memory ───┘
+```
+
+Multimodal world models are especially relevant to robotics, embodied AI, autonomous systems, Physical AI and interactive agents.
+
+---
+
+# 13. Spatial Representation
+
+Many environments require spatial understanding.
+
+A world model may need to represent:
+
+- depth
+- position
+- orientation
+- geometry
+- object permanence
+- navigation
+- 3D relationships
+- maps
+- trajectories
+- occlusion
+
+Spatial intelligence becomes increasingly important as world models move from passive prediction toward embodied interaction.
+
+---
+
+# 14. Time
+
+Time is fundamental to world modeling.
+
+```text
+Past → Present → Future
+```
+
+A world model may need to distinguish short-term dynamics, long-term dynamics, delayed consequences, irreversible actions, temporary events and persistent state.
+
+Temporal modeling may use recurrence, temporal attention, state-space models, autoregressive prediction or hierarchical time scales.
+
+---
+
+# 15. Uncertainty
+
+A world model should not assume that every future is known.
+
+Uncertainty comes from:
+
+- incomplete observations
+- stochastic dynamics
+- hidden variables
+- other agents
+- sensor noise
+- model error
+- unpredictable events
+
+Possible approaches include probabilistic latent variables, ensembles, diffusion, uncertainty heads and multiple trajectory hypotheses.
+
+---
+
+# Architecture Patterns
+
+There is no single canonical world-model architecture.
+
+## Pattern A — Encoder + Dynamics + Decoder
+
+```text
+Observation
+    ↓
+ Encoder
+    ↓
+Latent State
+    ↓
+Dynamics
+    ↓
+Future Latent State
+    ↓
+ Decoder
+    ↓
+Future Observation
+```
+
+## Pattern B — Latent Dynamics Only
+
+```text
+Observation
+    ↓
+ Encoder
+    ↓
+Latent State
+    ↓
+Dynamics
+    ↓
+Future Latent State
+```
+
+## Pattern C — Action-Conditioned World Model
+
+```text
+State + Action
+      ↓
+Dynamics Model
+      ↓
+Future State
+```
+
+## Pattern D — Generative World Model
+
+```text
+Context + Control
+       ↓
+Generative Model
+       ↓
+Possible Future Environment
+```
+
+## Pattern E — World Model + Planner
+
+```text
+Observation
+    ↓
+World Model
+    ↓
+Possible Futures
+    ↓
+Planner
+    ↓
+Action
+```
+
+## Pattern F — World Model + Policy
+
+```text
+World Model
+    ↓
+Imagined Experience
+    ↓
+Policy
+    ↓
+Action
+```
+
+## Pattern G — Unified World-Action Model
+
+```text
+Observation + Goal
+       ↓
+Unified Model
+       ├── Future State
+       └── Action
+```
+
+---
+
+# World Models and Reinforcement Learning
+
+World models are closely connected to **model-based reinforcement learning**.
+
+A model-free system learns directly from environment interaction.
+
+A model-based system can instead learn an environment model and use imagined trajectories.
+
+```text
+Environment
+    ↓
+Experience
+    ↓
+World Model
+    ↓
+Imagined Experience
+    ↓
+Policy / Planner
+```
+
+Potential benefits include improved sample efficiency, planning, exploration and long-horizon reasoning.
+
+Risks include model bias, compounding prediction errors and exploitation of incorrect imagined states.
+
+---
+
+# World Models and Robotics
+
+Robotics is one of the strongest application areas for world models.
+
+A robot may need to predict:
+
+- object motion
+- contact
+- collisions
+- grasp outcomes
+- tool behavior
+- human movement
+- navigation
+- physical constraints
+
+```text
+Perception
+    ↓
+World State
+    ↓
+Action Simulation
+    ↓
+Planning
+    ↓
+Robot Action
+```
+
+This is relevant to manipulation, humanoid robotics, mobile robotics, warehouse automation, autonomous vehicles and drones.
+
+---
+
+# World Models and AI Agents
+
+A digital AI agent also operates inside a world.
+
+That world may be a website, operating system, application, codebase, enterprise workflow, game or knowledge environment.
+
+```text
+Environment State
+      ↓
+World Model
+      ↓
+Possible Actions
+      ↓
+Predicted Outcomes
+      ↓
+Agent Planner
+      ↓
+Action
+```
+
+World modeling therefore connects naturally to agent architecture, memory, planning and tool use.
+
+---
+
+# World Models vs. Large Language Models
+
+A large language model primarily learns patterns over token sequences.
+
+A world model focuses more explicitly on environmental state, dynamics and possible transitions.
+
+| Large Language Model | World Model |
+|---|---|
+| Predicts tokens | Predicts states, trajectories or outcomes |
+| Primarily language-based | Can be visual, spatial, latent, multimodal or physical |
+| Strong at generation and reasoning | Strong focus on environment dynamics |
+| Often reactive to context | Can support simulation and planning |
+| Models textual structure | Models state transitions and consequences |
+
+Future systems may combine both:
+
+```text
+Language Model
+     +
+World Model
+     +
+Memory
+     +
+Planner
+     +
+Tools
+     +
+Actions
+```
+
+---
+
+# World Models vs. Video Models
+
+A video model may generate plausible future frames.
+
+A world model should ideally capture useful dynamics.
+
+Important questions include:
+
+- Are objects persistent?
+- Do actions have consistent consequences?
+- Is geometry stable?
+- Does the model preserve state?
+- Can it be used for planning?
+- Does it generalize beyond memorized visual patterns?
+
+A latent world model can be highly useful without producing photorealistic video.
+
+---
+
+# World Models vs. Digital Twins
+
+A digital twin usually represents a specific real-world asset, process or system.
+
+A world model is typically learned from data and focuses on dynamics.
+
+They can complement each other:
+
+```text
+Physical System
+      ↓
+Digital Twin
+      ↓
+Data / State
+      ↓
+World Model
+      ↓
+Prediction / Simulation / Planning
+```
+
+---
+
+# Training a World Model
+
+Training often requires sequences rather than isolated examples.
+
+```text
+Observationₜ
+Actionₜ
+Observationₜ₊₁
+```
+
+Repeated over time:
+
+```text
+Obs₀ → Action₀ → Obs₁ → Action₁ → Obs₂ → ...
+```
+
+Possible data sources include robot trajectories, games, video, simulation, autonomous-driving data, sensor logs, agent interaction logs, human demonstrations and synthetic environments.
+
+---
+
+# Training Objectives
+
+Common objectives may include:
+
+- reconstruction loss
+- next-state prediction
+- latent prediction
+- contrastive learning
+- masked prediction
+- diffusion loss
+- reward prediction
+- action prediction
+- inverse dynamics
+- forward dynamics
+- representation consistency
+
+Many systems combine several objectives.
+
+---
+
+# Evaluation
+
+World models should be evaluated according to the capability they are intended to provide.
+
+Useful dimensions include:
+
+## Prediction Accuracy
+Can the model predict future states accurately?
+
+## Temporal Consistency
+Does the world remain coherent over time?
+
+## Spatial Consistency
+Are position, geometry and object relationships preserved?
+
+## Action Fidelity
+Do actions produce the expected effects?
+
+## Long-Horizon Stability
+How quickly does prediction quality degrade?
+
+## Planning Utility
+Does the model improve decision making?
+
+## Control Performance
+Can an agent complete tasks using the model?
+
+## Generalization
+Does the model work outside the training distribution?
+
+## Uncertainty
+Can the model represent several plausible futures?
+
+## Efficiency
+What compute, memory and latency are required?
+
+---
+
+# Failure Modes
+
+World models can fail in important ways.
+
+## Compounding Error
+Small errors accumulate during long rollouts.
+
+## Model Bias
+The learned environment differs from the real environment.
+
+## Hallucinated Dynamics
+The model predicts plausible-looking but incorrect transitions.
+
+## Physical Inconsistency
+Generated states violate useful physical constraints.
+
+## Action Misalignment
+The model fails to represent the true consequence of actions.
+
+## Distribution Shift
+The system encounters unfamiliar environments or behaviors.
+
+## Hidden-State Failure
+Important information is missing from the internal state.
+
+## Overconfidence
+The model produces confident predictions despite uncertainty.
+
+---
+
+# Safety
+
+World models may influence decisions made by autonomous systems.
+
+Important considerations include:
+
+- uncertainty estimation
+- out-of-distribution detection
+- constraint checking
+- simulation validation
+- action verification
+- fallback policies
+- human oversight
+- model monitoring
+
+A simulated future should never automatically be treated as reality.
+
+---
+
+# A Practical Implementation Roadmap
+
+```text
+1. Define the environment
+        ↓
+2. Define observations
+        ↓
+3. Define actions
+        ↓
+4. Learn state representation
+        ↓
+5. Learn dynamics
+        ↓
+6. Predict future states
+        ↓
+7. Evaluate short-horizon quality
+        ↓
+8. Test long-horizon rollouts
+        ↓
+9. Add planning or control
+        ↓
+10. Validate in the target environment
+```
+
+Begin with the exact capability the system needs, not with architecture complexity.
+
+---
+
+# What This Organization Focuses On
+
+The **World Model** organization focuses on the technical building blocks behind predictive AI systems and on the engineering path from architecture to evaluation.
+
+```text
+WORLD MODEL
+│
+├── Observations
+├── Encoders
+├── State Representations
+├── Latent States
+├── Dynamics Models
+├── State Transitions
+├── Action Conditioning
+├── Future-State Prediction
+├── Rollouts
+├── Planning
+├── Policies
+├── Rewards / Values
+├── Memory
+├── Uncertainty
+├── Multimodal Inputs
+├── Spatial Representations
+├── Training
+├── Dataset Design
+├── Benchmark Design
+└── Evaluation
+```
+
+The practical structure of the organization mirrors that lifecycle:
+
+```text
+Choose an architecture
+        ↓
+Implement the core model
+        ↓
+Train predictive dynamics
+        ↓
+Evaluate short and long horizons
+        ↓
+Design reliable datasets and benchmarks
+        ↓
+Measure planning / control utility
+        ↓
+Iterate
+```
+
+The goal is to turn the abstract idea of a world model into a **clear, inspectable and reproducible system architecture**.
+
+---
+
+
+# Relationship to `world-models`
+
+The two Hugging Face organizations have deliberately different roles.
+
+## [`world-models`](https://huggingface.co/world-models)
+
+Focuses on the **field and ecosystem**:
+
+- models
+- research
+- datasets
+- benchmark families
+- categories
+- interactive resources
+- robotics and Physical AI
+- world action models
+- ecosystem mapping
+
+Its core question is:
+
+> **What exists in the world-model field, and how does it fit together?**
+
+## [`world-model`](https://huggingface.co/world-model)
+
+Focuses on the **engineering and internal structure**:
+
+- architecture
+- components
+- implementation
+- latent state
+- dynamics
+- action conditioning
+- prediction
+- rollouts
+- planning
+- training
+- dataset design
+- benchmark design
+- evaluation
+
+Its core question is:
+
+> **How does a world model actually work, and how can we build and evaluate one?**
+
+In short:
+
+```text
+world-models
+     ↓
+FIELD / ECOSYSTEM
+
+world-model
+     ↓
+ARCHITECTURE / ENGINEERING
+```
+
+Together they provide both the **ecosystem view** and the **technical implementation view**.
+
+---
+
+
+# Who Is This Organization For?
+
+World Model is intended for:
+
+- AI developers
+- ML engineers
+- researchers
+- students
+- robotics engineers
+- reinforcement-learning practitioners
+- agent developers
+- simulation engineers
+- autonomous-system developers
+- technical founders
+- anyone building predictive AI systems
+
+The material should remain useful even for people who are not specialists in model-based reinforcement learning.
+
+---
+
+# Frequently Asked Questions
+
+## What is the main component of a world model?
+
+There is no single required component, but most world models need some representation of state and some mechanism for predicting how that state changes.
+
+## Does a world model need a latent space?
+
+No. Some models operate directly in pixels, tokens, states or structured representations. Latent spaces are common because they can make prediction and planning more efficient.
+
+## Does a world model need actions?
+
+No. A predictive model can model future states without explicit action conditioning. But actions are essential for planning, control, robotics and interactive agents.
+
+## Does a world model need to generate images or video?
+
+No. A world model may operate entirely in latent or symbolic state space.
+
+## Can an LLM be a world model?
+
+Language models can contain substantial knowledge about how the world works, but a world model usually refers more specifically to a system that represents environment state, dynamics and possible future transitions. Hybrid systems are increasingly important.
+
+## Can a world model be used for AI agents?
+
+Yes. A digital agent can use a world model to represent software state, available actions and possible consequences.
+
+## Can a world model be used for robotics?
+
+Yes. Robotics is one of the most important application areas because robots need to predict how physical environments change as a result of actions.
+
+## Why are long rollouts difficult?
+
+Prediction errors compound. A small mistake at one step can become a large deviation several steps later.
+
+## What is action conditioning?
+
+Action conditioning means the predicted future depends on the action being considered.
+
+## What is a latent dynamics model?
+
+A latent dynamics model predicts how compressed internal state representations evolve through time.
+
+---
+
+# Glossary
+
+**Action Conditioning**  
+Making future-state predictions depend on an action or control input.
+
+**Dynamics Model**  
+A model that predicts how a state evolves over time.
+
+**Encoder**  
+A component that converts observations into a learned representation.
+
+**Latent State**  
+A compressed internal representation of an environment.
+
+**Planner**  
+A system that evaluates possible actions or trajectories to achieve a goal.
+
+**Policy**  
+A function or model that selects an action from a state.
+
+**Rollout**  
+A sequence of predicted future states generated by repeatedly applying the world model.
+
+**State**  
+A representation of the current condition of an environment.
+
+**State Transition**  
+A change from one state to another.
+
+**World Model**  
+A learned representation of an environment and its dynamics used for prediction, simulation, planning or control.
+
+---
+
+# Cooperation
+
+We welcome conversations and collaboration around:
+
+- world-model architectures
+- predictive systems
+- latent representations
+- dynamics models
+- action-conditioned models
+- planning and control
+- robotics
+- reinforcement learning
+- AI agents
+- simulation
+- training methods
+- datasets
+- benchmark design
+- evaluation
+- uncertainty and reliability
+- open-source infrastructure
+
+Potential cooperation can include:
+
+- research resources
+- open-source projects
+- technical reference implementations
+- dataset and benchmark initiatives
+- ecosystem mapping
+- educational resources
+- model and tooling integrations
+
+**Cooperation, research and ecosystem partnerships:**  
+📩 [agenten@magenta.de](mailto:agenten@magenta.de)
+
+---
+
+## World Model
+
+**Represent the state. Learn the dynamics. Predict the future. Plan the action.**