W
A S D
00:00
-10:00
W A S D
W A S D
W A S D
W A S D
W A S D
W A S D
W A S D
W A S D
W A S D
W A S D

What is LingBot-World?

Open-source world model by Robbyant (Ant Group)

28B Parameters
16 FPS
<1s Latency
10min+ Stable
720P Output
Apache 2.0 License

Unlike video generation models that produce passive content, LingBot-World creates interactive worlds in real-time. Objects persist when you look away, physics behave consistently, and the world responds to your every action.

Three Breakthrough Features That Define World Model Excellence

Discover the technical innovations that make LingBot-World the leading open-source world model, rivaling Google Genie 3 in every metric that matters

Stable Long-term Memory

The most critical capability for any world model. Without it, you'd experience "ghost walls" - turn around and the door disappears, turn back and the toilet becomes a little girl staring at you.

  • 10+ minutes of stable generation without collapse
  • Buildings stay consistent when you look away and back
  • Proper occlusion relationships maintained
  • Correct time and distance scaling
Benchmark: 10-minute ancient architecture exploration with no world collapse

Extreme Style Generalization

Most world models only work with photorealistic content. LingBot-World maintains quality across diverse visual styles thanks to its unique multi-domain training approach.

  • Photorealistic environments
  • Anime and cartoon styles
  • Game-quality visuals
  • Fantasy and sci-fi worlds
Training Data: Real videos + Game recordings + UE synthetic scenes

Intelligent Action Agent

Beyond simple walking simulators. LingBot-World features an AI agent that can autonomously navigate and interact with the generated world, creating emergent gameplay experiences.

  • WASD keyboard controls for manual navigation
  • Continuous motion understanding (not frame-by-frame)
  • VLM-powered autonomous agent
  • Collision detection and avoidance
Innovation: AI plays its own world - observe while the agent explores

Emerging Capabilities

As our world model scales, we observe the emergence of sophisticated behaviors that go beyond simple video generation

Dynamic Off-Screen Memory

Beyond simple object permanence, the model maintains a persistent memory of agents that continue to act even when unobserved. This ensures that when the view returns, the world state has progressed naturally rather than freezing in place.

Exploring the Generation Boundary

Pushing the boundaries of temporal coherence, our model can now sustain stable, high-fidelity environments for ultra-long video generation without degrading.

Grounded Physical Constraints

The model enforces realistic collision dynamics, preventing agents from clipping through obstacles or ignoring solid barriers. This adherence to spatial logic ensures that movement remains physically plausible.

Transform AAA Game Development with AI World Models

Zero-code world generation, massive cost reduction, and infinite possibilities for game creators using LingBot-World's revolutionary AI technology

The gaming industry faces an unprecedented challenge: AAA game development costs have spiraled to hundreds of millions of dollars, with companies like Ubisoft, Sony, and Microsoft shutting down studios and canceling projects. LingBot-World offers a transformative solution by enabling game developers to generate fully interactive, physics-compliant game environments from simple inputs - no 3D modeling, no asset creation pipelines, no months of environment art production. This world model technology represents the future of game development, where AI handles environment generation while developers focus on gameplay innovation.

Rapid Prototyping

Build core gameplay demos without writing a single line of code. Test mechanics like Zelda's "Ultrahand" by describing the functionality - the world model handles physics, interactions, and visuals.

Dramatically reduced iteration cycles

Automated QA Testing

Generate diverse virtual environments for large-scale automated testing. Detect physics collision bugs, logic errors, and edge cases across thousands of procedurally generated scenarios.

Physics-compliant test environments

Intelligent NPC Training

Train AI agents in dynamically generated worlds. Create high-intelligence NPCs by having them learn navigation, interaction, and decision-making in realistic simulated environments.

Train agents in generated worlds

Infinite Open Worlds

Create truly infinite, logically consistent open worlds. The environment generates as players explore - no pre-built assets needed. Change seasons, weather, or entire biomes with simple prompts.

Procedural generation on-the-fly

Dynamic World Modification

Change any aspect of your generated world through simple text prompts

Weather "Add rain" "Clear skies"
🍁 Seasons "Winter snow" "Autumn leaves"
🎆 Effects "Fireworks" "Lightning"
🐟 Objects "Add fish to fountain"
🏘 Structures "Place a castle"
💡 Triggers "Fireworks near castle"

Transforming Game Development Economics

How world models are reshaping the future of interactive content creation

30-40% Traditional art asset budget

AAA games spend nearly half their budget on environment art, character models, and world assets.

~55% Potential cost reduction

World models can dramatically reduce environment creation costs while accelerating iteration cycles.

10min+ Content from one image

Generate over 10 minutes of explorable, physics-compliant world content from a single reference image.

Infinite Procedural worlds

Create truly infinite open worlds that generate as players explore - no pre-built assets required.

The gaming industry is at a crossroads. Rising development costs have forced studios to shut down projects, lay off teams, and delay releases. World models like LingBot-World offer a path forward - enabling smaller teams to create expansive, AAA-quality environments while focusing resources on gameplay innovation rather than asset production pipelines.

Download LingBot-World: Three Model Variants for Every Use Case

Choose the right LingBot-World version for your project - from camera-controlled exploration to real-time interactive generation

Available Now

LingBot-World-Base

(Camera Poses)

Control camera movement with precise pose trajectories. Perfect for cinematic shots, environment scanning, and controlled exploration.

Resolution 480P / 720P
Parameters ~28B
Inference ~14B
  • Camera pose control
  • Orbit, pan, tilt movements
  • Dolly and tracking shots
  • Custom trajectory input
View on HuggingFace
Coming Soon

LingBot-World-Base

(Actions)

Control subject behavior with structured action commands. Specify movements, gestures, and interactions at the behavioral level.

Control Action Commands
Parameters ~28B
Inference ~14B
  • Behavioral control
  • Movement commands
  • Gesture specification
  • Turn, walk, run actions

Real-time Demo Gallery

Watch LingBot-World generate diverse, explorable worlds in real-time across multiple visual styles

World Modification Showcase

Select a scene and apply events to see how LingBot-World handles dynamic world modifications

What Event Should Happen Next?

Select Scene:

Event:

Action Agent

Autonomous agents that plan and execute actions within the generated world

W A S D
W A S D
W A S D
W A S D
W A S D

3D Reconstruction

The model can reconstruct 3D geometry from generated world views

Scene 1

Scene 2

Scene 3

Technical Architecture: How LingBot-World Works

Explore the technical specifications, training methodology, and architectural innovations that power LingBot-World's state-of-the-art world model capabilities

Architecture

  • Model Size: ~28 billion parameters
  • Inference Size: ~14 billion parameters
  • Input: Video frames + Camera poses/Actions + Text
  • Output: Real-time generated video frames

Training Data

  • Real Videos: Physical world appearance and behavior
  • Game Recordings: How humans interact in virtual worlds
  • UE Synthetic: Extreme camera paths and edge cases
  • Domain Randomization: Like robotics sim-to-real transfer

Key Innovations

  • Long-term Memory: Maintains world consistency over 10+ minutes
  • Continuous Actions: Motion as intention, not single frames
  • VLM Agent: Fine-tuned vision-language model for autonomous navigation
  • Multi-domain Learning: Unified training across visual styles

Applications

  • Gaming: Infinite procedural world generation
  • Film/VFX: Pre-visualization and virtual production
  • Embodied AI: Low-cost training simulation for robots
  • Entertainment: Interactive storytelling experiences

Comparison with Competitors

Feature LingBot-World Google Genie 3 Odyssey
Open Source ✓ Yes ✗ No (Closed) ✗ No
Public Access ✓ Deploy Now ✗ Research Only ✓ Limited
Verified Demo Length 10+ minutes ~1 minute shown <1 minute
Memory Consistency Excellent Excellent Poor (Ghost walls)
Physics Simulation Spacetime aware Strong Pixel-based only
Off-screen Inference ✓ Objects persist ✓ Yes ✗ Objects vanish
Style Variety Multiple styles Good Limited
Action Agent ✓ VLM-based ✗ Unknown ✗ No
API Available ✓ Open ✗ No public API ✗ Limited

Key Advantage: While Genie 3 showcases similar technical capabilities, LingBot-World is the first SOTA-level world model that's fully open-source and deployable, allowing developers and researchers to build upon it immediately.

Limitations And Next Steps

While the model demonstrates significant potential, several technical constraints remain. The high inference cost currently necessitates enterprise-grade GPUs, making the technology inaccessible on consumer hardware. Additionally, because memory is emergent from the context window rather than an explicit storage module, the simulation lacks long-term stability; this often leads to environmental drifting where the scene gradually loses structural integrity over extended durations. Control capabilities are also restricted to basic navigation, lacking the fine-grained precision required for complex interactions or specific object manipulation. Finally, achieving real-time performance through causal distillation currently requires a trade-off that slightly degrades visual fidelity.

Looking ahead, our roadmap prioritizes expanding the action space and physics engine to support diverse, complex interactions. To ensure long-term stability, we aim to implement an explicit memory module rather than relying on emergent context. Furthermore, we are focused on eliminating generation drift, paving the way for robust, infinite-time gameplay and more robust simulations.

How to Install and Run LingBot-World: Quick Start Guide

Get LingBot-World running on your system in four simple steps - from cloning the repository to generating your first AI world

1

Clone the Repository

git clone https://github.com/Robbyant/lingbot-world.git
2

Download Model Weights

Download the Base (Cam) model weights from the official release page.

3

Install Dependencies

pip install -r requirements.txt
4

Run Inference

python inference.py --model base_cam --resolution 720p

The Future of Interactive AI Worlds

LingBot-World represents a paradigm shift from passive video generation to active world simulation. Join the community and help shape the future of AI-generated interactive experiences.