🌳 World Models Evolutionary Tree

🔥 News & Updates

• [2026-03-06] 🎉 Repository launched! Unified collection of 489 papers from four major World Models repositories
• [2026-03-06] 🆕 Added 11 latest papers from arXiv 2026 (LaST-VLA, ResWorld, DriveWorld-VLA, etc.)
• [2026-03-06] 📊 Added comprehensive statistics and visualizations: 74 starred papers, 195 with code
• [2026-03-06] 🗂️ Introduced a strict paper-only research taxonomy plus dedicated resource sections
• [2026-03-06] 🤖 Integrated learning resources: talks, courses, tutorials, and datasets

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📖 Table of Contents

• 💡 What are World Models?
• 🗺️ Taxonomy
• 📚 Research
  • Surveys & Reviews
  • Theory & Foundations
  • Benchmarks & Evaluation
  • Primary Research by Domain
    • 🌐 General / Foundational
    • 🚗 Autonomous Driving
    • 🤖 Embodied AI & Robotics
    • 🎮 Interactive Digital Environments
    • 👥 Social / Multi-Agent
    • 🔬 Scientific World Models
• 🎓 Learning Resources
  • 📺 Talks & Presentations
  • 🎓 Courses & Tutorials
• 🔧 Practical Resources
  • 📊 Datasets
  • 🎯 Benchmarks & Leaderboards
  • 🛠️ Tools & Libraries
• 🌐 Community
  • 🏆 Workshops & Challenges
  • 👥 Research Groups
  • 💬 Discussion Spaces
• 🤝 Contributing
• 📜 Citation
• ⭐ Star History

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💡 What are World Models?

World Models are AI systems that learn internal representations of their environment to predict future states, simulate scenarios, and enable intelligent decision-making. They bridge perception and action by building a mental model of how the world works.

Key Concepts

• Predictive Modeling: Learning to forecast future observations from current state and actions
• Latent Representations: Compressing high-dimensional sensory data into meaningful internal states
• Simulation: Generating synthetic experiences for planning, training, and evaluation
• Generalization: Transferring learned world knowledge to new scenarios and tasks

Why World Models Matter

1. Data Efficiency: Learn from fewer real-world interactions by leveraging simulated experience
2. Safety: Test dangerous scenarios in simulation before deployment
3. Interpretability: Explicit world representations enable better understanding of AI decisions
4. Generalization: Transfer knowledge across tasks and domains
5. Planning: Enable look-ahead reasoning for complex decision-making

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🗺️ Taxonomy

This repository uses a strict two-track information architecture:

Track 1: Research taxonomy (paper-only)

All entries under Research are papers only, and each paper appears in exactly one canonical place:

• Surveys & Reviews
• Theory & Foundations
• Benchmarks & Evaluation
• Primary Research by Domain
  • General / Foundational
  • Autonomous Driving
  • Embodied AI & Robotics
  • Interactive Digital Environments
  • Social / Multi-Agent
  • Scientific World Models

Track 2: Resource taxonomy (non-paper)

Non-paper resources are preserved in dedicated sections:

• Learning Resources: talks, tutorials, and courses
• Practical Resources: datasets, benchmarks, tools, libraries, and simulators
• Community: workshops, challenges, research groups, and discussion spaces

For paper classification, labels such as VLA, navigation, locomotion, planning/RL, simulation, and web-agent are treated as tags, not canonical top-level categories.

📖 For the authoritative classification rules, see docs/research/taxonomy.md

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📚 Research

Surveys & Reviews

Comprehensive surveys and review papers on world models:

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Theory & Foundations

Conceptual, analytical, and foundational papers on world models.

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Benchmarks & Evaluation

Benchmark, dataset, and evaluation papers for assessing world models.

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Primary Research by Domain

🌐 General / Foundational

Primary research papers whose core contribution is broadly reusable across domains.

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🚗 Autonomous Driving

World models for scene prediction, planning, simulation, and control in self-driving systems.

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🤖 Embodied AI & Robotics

World models for manipulation, navigation, locomotion, VLA systems, and robot policy learning.

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🎮 Interactive Digital Environments

World models for games, browser environments, web agents, and other interactive virtual settings.

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👥 Social / Multi-Agent

World models centered on social interaction, collective behavior, or multi-agent reasoning.

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🔬 Scientific World Models

World models applied to scientific simulation, medicine, biology, and related domains.

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🎓 Learning Resources

📺 Talks & Presentations

Key talks and presentations on world models:

📺 For complete list of talks (50+), see docs/learning/talks.md

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🎓 Courses & Tutorials

New to world models? Start with the beginner path below instead of jumping straight into papers or large codebases.

Beginner Journey:

1. Build intuition — World Models and A Path Towards Autonomous Machine Intelligence
2. Learn the core loop — CS285: Deep Reinforcement Learning and DreamerV3
3. Pick a track — model-based RL, autonomous driving, or embodied AI / robotics

Choose your first track:

• General / Model-Based RL — start with Dreamer-style latent dynamics and planning
• Autonomous Driving — start with CARLA, then driving world-model papers and benchmarks
• Embodied AI / Robotics — start with CALVIN / LIBERO, then VLA and WAM systems

🎓 For the full beginner-friendly learning path, curated tracks, and hands-on starting points, see docs/learning/tutorials.md

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🔧 Practical Resources

📊 Datasets

Autonomous Driving:

• nuScenes - 1000 scenes with camera, LiDAR, radar
• Waymo Open Dataset - 1000 segments, 200k frames
• KITTI - Classic autonomous driving benchmark
• Argoverse 2 - 1000 scenarios, forecasting
• Occ3D - 16k frames for occupancy prediction
• CARLA - Open-source driving simulator

Robotics:

• CALVIN - 24k manipulation trajectories
• RoboNet - 15M robot manipulation frames
• RoboCasa - 100k kitchen manipulation trajectories
• Open X-Embodiment - 1M+ multi-robot trajectories
• Habitat-Matterport 3D - 90 indoor scenes

Games:

• Minecraft - Procedural 3D environments
• Atari 2600 - Classic RL benchmark (57 games)
• MineRL - 60M frames of Minecraft gameplay

📊 For complete list of datasets (50+), see docs/resources/datasets.md

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🎯 Benchmarks & Leaderboards

Driving:

• WorldLens - Full-spectrum evaluation for driving world models
• DrivingGen - Benchmark for generative video world models in autonomous driving
• NAVSIM - End-to-end planning benchmark for autonomous driving

Embodied / Robotics:

• LIBERO - Knowledge transfer benchmark for lifelong robot learning
• CALVIN - Language-conditioned manipulation benchmark
• RoboCasa - Everyday manipulation benchmark in realistic simulation
• RoboTwin 2.0 - Bimanual manipulation benchmark with strong domain randomization

Video & World Generation:

• WorldScore - Unified benchmark for world generation
• VBench - Video generation quality evaluation
• FVD / LPIPS / PSNR / SSIM - Common quality metrics for rollout fidelity and perceptual realism

Multi-Agent:

• Melting Pot - Generalization-oriented multi-agent RL evaluation suite

🎯 For complete benchmark suites, metrics, and leaderboards, see docs/resources/benchmarks.md

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🛠️ Tools & Libraries

Frameworks:

• DreamerV3 - State-of-the-art model-based RL
• Stable Baselines3 - RL baselines and integration utilities
• PyTorch3D - 3D deep learning library

Simulation:

• CARLA - Open-source driving simulator
• Isaac Sim - NVIDIA robotics simulator
• MuJoCo - Physics engine for robotics

🛠️ For complete tools, libraries, and simulators, see docs/resources/tools.md

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🌐 Community

🏆 Workshops & Challenges

• CVPR 2025 Workshop on World Models
• ICCV 2025 Workshop on 4D World Models - Bridging Generation and Reconstruction
• OpenDriveLab Challenges - Annual autonomous driving competitions

🏆 For a fuller list of workshops and challenge venues, see docs/community/workshops.md

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👥 Research Groups

Leading Labs:

• NVIDIA Toronto AI Lab - Cosmos, GAIA series
• Wayve - End-to-end driving with world models
• Tesla AI - FSD world model development
• UC Berkeley RAIL - Model-based RL research
• DeepMind - Genie, DreamerV3

👥 For a fuller list of research groups and labs, see docs/community/research-groups.md

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💬 Discussion Spaces

• r/MachineLearning - Broad ML discussion and paper sharing
• Hugging Face Forums - Model discussion and implementation questions
• Papers with Code - Benchmark and code discovery

💬 For additional communities and curated hubs, see docs/community/communities.md

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🤝 Contributing

Please use the contributor guide for submission rules, taxonomy requirements, and PR templates.

• Read CONTRIBUTING.md

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📜 Citation

If you find this repository useful, please consider citing:

@misc{awesome-world-models-2026,
  title={Awesome World Models: A Comprehensive Collection},
  author={Jing, Bowen},
  year={2026},
  howpublished={\url{https://github.com/Bowen12137/Awesome-World-Models}}
}

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⭐ Star History

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📊 Repository Statistics

Overview

• Total Papers: 269
• Starred Papers: 88
• Papers with Code: 111 (41%)
• Year Range: 2021 - 2026
• Top Venues: AAAI (3), CVPR (2), ECCV (2)

Visualizations

Last Updated: March 6, 2026

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