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World-Model

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[ACM CSUR 2025] Understanding World or Predicting Future? A Comprehensive Survey of World Models

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# Awesome-World-Model [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A curated list of awesome resources on World Models, based on the comprehensive survey "Understanding World or Predicting Future? A Comprehensive Survey of World Models".

![Loading roadmap](https://raw.githubusercontent.com/tsinghua-fib-lab/World-Model/main/asset/structure.png "outline")

## News🔥

* **[2024/11/21]** Initial release of our survey is available on [arXiv](https://arxiv.org/abs/2411.14499v1).
* **[2025/06/13]** Our survey paper "Understanding World or Predicting Future? A Comprehensive Survey of World Models" has been accepted by [ACM Computing Surveys](https://dl.acm.org/doi/abs/10.1145/3746449).
* **[2025/06/25]** Second version of our survey is available on [arXiv](https://arxiv.org/abs/2411.14499).
* **[2025/07/18]** Initial release of the Awesome-World-Model GitHub repository.
* **[2025/11/18]** Third version of our survey is available on  [arXiv](https://arxiv.org/abs/2411.14499).

## Contact
If you have any suggestions or find our work helpful, feel free to contact us  
Email: dingjt15@tsinghua.org.cn

If this list helps your research, please ⭐ and cite:

```bibtex
@article{ding2025understanding,
  title={Understanding World or Predicting Future? A Comprehensive Survey of World Models},
  author={Ding, Jingtao and Zhang, Yunke and Shang, Yu and Zhang, Yuheng and Zong, Zefang and Feng, Jie and Yuan, Yuan and Su, Hongyuan and Li, Nian and Sukiennik, Nicholas and others},
  journal={ACM Computing Surveys},
  volume={58},
  number={3},
  pages={1--38},
  year={2025},
  publisher={ACM New York, NY}
}
```

## Table of Contents 🍃

* [Roadmap of world models in deep learning era](#Roadmap-of-world-models-in-deep-learning-era)
    * [Model-based RL](#Model-based-RL)
    * [Self-supervised learning](#Self-supervised-learning)
    * [LLM/MLLM](#LLMMLLM)
    * [Interactive 3D environment](#Interactive-3D-environment)

* [3 Implicit Representation of the External World](#3-implicit-representation-of-the-external-world)
    * [3.1 World Model in Decision Making](#31-World-Model-in-Decision-Making)
    * [3.2 World Knowledge Learned by Models](#32-World-Knowledge-Learned-by-Models)
* [4 Future Prediction of the Physical World](#4-future-predictions-of-the-external-world)
    * [4.1 World Model as Video Generation](#41-World-Model-as-Video-Generation)
    * [4.2 World Model as Embodied Environment](#42-World-Model-as-Embodied-Environment)
* [5 Applications of World Models](#5-applications-of-world-models)
    * [5.1 Game Intelligence](#51-Game-Intelligence)
    * [5.2 Embodied Intelligence](#52-Embodied-Intelligence)
    * [5.3 Urban Intelligence](#53-Urban-Intelligence)
    * [5.4 Societal Intelligence](#54-Societal-Intelligence)

## Roadmap of world models in deep learning era

![Loading roadmap](https://raw.githubusercontent.com/tsinghua-fib-lab/World-Model/main/asset/roadmap.png "roadmap")

### Model-based RL

| Title                                                        | Pub. & Date  | Code/Project URL                                             |
| ------------------------------------------------------------ | ------------ | ------------------------------------------------------------ |
| [Recurrent world models facilitate policy evolution  **(RWM)**](https://proceedings.neurips.cc/paper/2018/file/2de5d16682c3c35007e4e92982f1a2ba-Paper.pdf) | NeurIPS 2018 | [![Website](https://img.shields.io/badge/Website-9cf)](https://worldmodels.github.io/) |
| [Learning Latent Dynamics for Planning from Pixels  **(PlaNet)**](https://arxiv.org/pdf/1811.04551) | ICML 2019    | [![Star](https://img.shields.io/github/stars/google-research/planet.svg?style=social&label=Star)](https://github.com/google-research/planet) |
| [Dream to control: Learning behaviors by latent imagination  **(Dreamer V1)**](https://arxiv.org/pdf/1912.01603) | ICLR 2020    | [![Star](https://img.shields.io/github/stars/danijar/dreamer.svg?style=social&label=Star)](https://github.com/danijar/dreamer) |
| [Mastering atari with discrete world models  **(Dreamer V2)**](https://arxiv.org/pdf/2010.02193) | ICLR 2021    | [![Star](https://img.shields.io/github/stars/danijar/dreamerv2.svg?style=social&label=Star)](https://github.com/danijar/dreamerv2) |
| [Temporal Difference Learning for Model Predictive Control  **(TD-MPC1)**](https://arxiv.org/pdf/2203.04955) | ICML 2023    | [![Star](https://img.shields.io/github/stars/nicklashansen/tdmpc.svg?style=social&label=Star)](https://github.com/nicklashansen/tdmpc) |
| [Mastering Diverse Domains through World Models  **(Dreamer V3)**](https://arxiv.org/pdf/2301.04104) | 2023         | [![Star](https://img.shields.io/github/stars/danijar/dreamerv3.svg?style=social&label=Star)](https://github.com/danijar/dreamerv3) |
| [TD-MPC2: Scalable, Robust World Models for Continuous Control  **(TD-MPC2)**](https://openreview.net/pdf?id=Oxh5CstDJU) | ICLR 2024    | [![Star](https://img.shields.io/github/stars/nicklashansen/tdmpc2.svg?style=social&label=Star)](https://github.com/nicklashansen/tdmpc2) |
| [PWM: Policy Learning with Multi-Task World Models  **(PWM)**](https://openreview.net/pdf?id=Oxh5CstDJU) | ICLR 2025    | [![Star](https://img.shields.io/github/stars/imgeorgiev/PWM.svg?style=social&label=Star)](https://github.com/imgeorgiev/PWM) |

### Self-supervised learning

| Title                                                        | Pub.&Date | Code/Project URL                                             |
| ------------------------------------------------------------ | --------- | ------------------------------------------------------------ |
| [A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27  **(JEPA)**](https://openreview.net/pdf?id=BZ5a1r-kVsf) | 2024      | —                                                            |
| [DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning  **(DINO-WM)**](https://arxiv.org/pdf/2411.04983) | 2024      | [![Star](https://img.shields.io/github/stars/gaoyuezhou/dino_wm.svg?style=social&label=Star)](https://github.com/gaoyuezhou/dino_wm) |
| [Revisiting Feature Prediction for Learning Visual Representations from Video  **(V-JEPA)**](https://arxiv.org/pdf/2404.08471) | 2024      | [![Star](https://img.shields.io/github/stars/facebookresearch/jepa.svg?style=social&label=Star)](https://github.com/facebookresearch/jepa) |
| [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning  **(V-JEPA2)**](https://arxiv.org/pdf/2506.09985) | 2025      | [![Star](https://img.shields.io/github/stars/facebookresearch/vjepa2.svg?style=social&label=Star)](https://github.com/facebookresearch/vjepa2) |

### LLM/MLLM

| Title                                                        | Pub.&Date    | Code/Project URL                                             |
| ------------------------------------------------------------ | ------------ | ------------------------------------------------------------ |
| [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning  **(LLM-DM)**](https://proceedings.neurips.cc/paper_files/paper/2023/file/f9f54762cbb4fe4dbffdd4f792c31221-Paper-Conference.pdf) | NeurIPS 2023 | [![Star](https://img.shields.io/github/stars/GuanSuns/LLMs-World-Models-for-Planning.svg?style=social&label=Star)](https://github.com/GuanSuns/LLMs-World-Models-for-Planning) |
| [WorldGPT: Empowering LLM as Multimodal World Model  **(WorldGPT)**](https://dl.acm.org/doi/pdf/10.1145/3664647.3681488) | ACM MM 2024  | [![Star](https://img.shields.io/github/stars/DCDmllm/WorldGPT.svg?style=social&label=Star)](https://github.com/DCDmllm/WorldGPT) |
| [Text2World: Benchmarking Large Language Models for Symbolic World Model Generation  **(Text2World)**](https://arxiv.org/pdf/2502.13092) | ACL 2025     | [![Star](https://img.shields.io/github/stars/Aaron617/text2world.svg?style=social&label=Star)](https://github.com/Aaron617/text2world) |

### Video generation

| Title                                                        | Pub.&Date    | Code/Project URL                                             |
| ------------------------------------------------------------ | ------------ | ------------------------------------------------------------ |
| [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers  **(CogVideo)**](https://arxiv.org/pdf/2205.15868) | ICLR 2023    | [![Star](https://img.shields.io/github/stars/zai-org/CogVideo.svg?style=social&label=Star)](https://github.com/zai-org/CogVideo) |
| [Structure and Content-Guided Video Synthesis with Diffusion Models  **(Gen‑1)**](https://arxiv.org/pdf/2508.08086) | ICCV 2023    | [![Website](https://img.shields.io/badge/Website-9cf)](https://runwayml.com/research/gen-1) |
| [UniSim: Learning Interactive Real-World Simulators  **(Unisim)**](https://openreview.net/pdf?id=sFyTZEqmUY) | ICLR 2024    | [![Website](https://img.shields.io/badge/Website-9cf)](https://universal-simulator.github.io/unisim/) |
| [Sora: Creating video from text  **(Sora)**](https://openai.com/sora) | OpenAI 2024  | —                                                            |
| [World model on million-length video and language with ring-attention  **(LWM)**](https://arxiv.org/pdf/2402.08268) | ICLR 2025    | [![Star](https://img.shields.io/github/stars/LargeWorldModel/lwm.svg?style=social&label=Star)](https://github.com/LargeWorldModel/lwm) |
| [Genie: Generative Interactive Environmentsn  **(Genie)**](https://openreview.net/pdf?id=bJbSbJskOS) | ICML 2024    | [![Website](https://img.shields.io/badge/Website-9cf)](https://sites.google.com/view/genie-2024/home) |
| [iVideoGPT: Interactive VideoGPTs are Scalable World Models  **(iVideoGPT)**](https://arxiv.org/pdf/2405.15223) | NeurIPS 2024 | [![Star](https://img.shields.io/github/stars/thuml/iVideoGPT.svg?style=social&label=Star)](https://github.com/thuml/iVideoGPT) |
| [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer  **(CogVideoX)**](https://arxiv.org/pdf/2408.06072) | ICLR 2025    | [![Star](https://img.shields.io/github/stars/zai-org/CogVideo.svg?style=social&label=Star)](https://github.com/zai-org/CogVideo) |
| [Wan: Open and Advanced Large-Scale Video Generative Models  **(Wan)**](https://arxiv.org/pdf/2507.21809) | 2025         | [![Star](https://img.shields.io/github/stars/Wan-Video/Wan2.1.svg?style=social&label=Star)](https://github.com/Wan-Video/Wan2.1) |
| [Cosmos World Foundation Model Platform for Physical AI  **(Cosmos)**](https://arxiv.org/pdf/2501.03575) | 2025         | [![Star](https://img.shields.io/github/stars/nvidia-cosmos/cosmos-predict2.svg?style=social&label=Star)](https://github.com/nvidia-cosmos/cosmos-predict2) |

### Interactive 3D environment

| Title                                                        | Pub.&Date | Code/Project URL                                             |
| ------------------------------------------------------------ | --------- | ------------------------------------------------------------ |
| [Interactive 3D Scene Generation from a Single Image  **(WonderWorld)**](https://arxiv.org/pdf/2308.07540) | CVPR 2025 | [![Star](https://img.shields.io/github/stars/KovenYu/WonderWorld.svg?style=social&label=Star)](https://github.com/KovenYu/WonderWorld) |
| [Matrix-3D: Omnidirectional Explorable 3D World Generation  **(Matrix-3D)**](https://arxiv.org/pdf/2508.08086) | 2025      | [![Star](https://img.shields.io/github/stars/SkyworkAI/Matrix-3D.svg?style=social&label=Star)](https://github.com/SkyworkAI/Matrix-3D) |
| [HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels  **(Hunyuan World)**](https://arxiv.org/pdf/2507.21809) | 2025      | [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/HunyuanWorld-1.0.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0) |

### Application

| Title                                                        | Pub.&Date           | Code/Project URL                                             |
| ------------------------------------------------------------ | ------------------- | ------------------------------------------------------------ |
| [DayDreamer: World Models for Physical Robot Learning  **(DayDreamer)**](https://arxiv.org/pdf/2206.14176) | 2023                | [![Star](https://img.shields.io/github/stars/danijar/daydreamer.svg?style=social&label=Star)](https://github.com/danijar/daydreamer) |
| [Generative Agents: Interactive Simulacra of Human Behavior  **(Generative Agents)**](https://arxiv.org/pdf/2304.03442) | UIST 2023           | [![Star](https://img.shields.io/github/stars/joonspk-research/generative_agents.svg?style=social&label=Star)](https://github.com/joonspk-research/generative_agents) |
| [GAIA-1: A generative world model for autonomous driving  **(GAIA-1)**](https://arxiv.org/pdf/2309.17080) | 2023                | [![Website](https://img.shields.io/badge/Website-9cf)](https://anthonyhu.github.io/gaia1) |
| [OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving  **(OccWorld)**](https://arxiv.org/pdf/2311.16038) | ECCV 2024           | [![Star](https://img.shields.io/github/stars/wzzheng/OccWorld.svg?style=social&label=Star)](https://github.com/wzzheng/OccWorld) |
| [Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation  **(GR1)**](https://arxiv.org/pdf/2312.13139) | ICLR 2024           | [![Star](https://img.shields.io/github/stars/bytedance/GR-1.svg?style=social&label=Star)](https://github.com/bytedance/GR-1) |
| [DriveDreamer: Towards real-world-driven world models  **(DriveDreamer)**](https://arxiv.org/pdf/2309.09777) | ECCV 2024           | [![Star](https://img.shields.io/github/stars/JeffWang987/DriveDreamer.svg?style=social&label=Star)](https://github.com/JeffWang987/DriveDreamer) |
| [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving  **(Drive-WM)**](https://arxiv.org/pdf/2311.17918) | CVPR 2024           | [![Star](https://img.shields.io/github/stars/BraveGroup/Drive-WM.svg?style=social&label=Star)](https://github.com/BraveGroup/Drive-WM) |
| [Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)  **(Think2Drive)**](https://arxiv.org/pdf/2311.17918) | ECCV 2024           | [![Website](https://img.shields.io/badge/Website-9cf)](https://thinklab-sjtu.github.io/CornerCaseRepo/) |
| [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving  **(Drive-WM)**](https://arxiv.org/pdf/2311.17918) | CVPR 2024           | [![Star](https://img.shields.io/github/stars/BraveGroup/Drive-WM.svg?style=social&label=Star)](https://github.com/BraveGroup/Drive-WM) |
| [GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving  **(GaussianWorld)**](https://arxiv.org/pdf/2412.10373) | CVPR 2025           | [![Star](https://img.shields.io/github/stars/zuosc19/GaussianWorld.svg?style=social&label=Star)](https://github.com/zuosc19/GaussianWorld) |
| [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving  **(Drive-WM)**](https://arxiv.org/pdf/2311.17918) | 2025                | [![Website](https://img.shields.io/badge/Website-9cf)](https://wayve.ai/thinking/gaia-2/) |
| [World and Human Action Models towards Gameplay Ideation  **(WHAM)**](https://www.nature.com/articles/s41586-025-08600-3.pdf) | Nature 2025         | —                                                            |
| [Mineworld: a Real-time and Open-source Interactive World Model on Minecraft  **(MineWorld)**](https://arxiv.org/pdf/2504.08388?) | 2025                | [![Star](https://img.shields.io/github/stars/microsoft/MineWorld.svg?style=social&label=Star)](https://github.com/microsoft/MineWorld) |
| [GameFactory: Creating New Games with Generative Interactive Videos  **(Gamefactory)**](https://arxiv.org/pdf/2501.08325) | ICCV 2025           | [![Star](https://img.shields.io/github/stars/KwaiVGI/GameFactory.svg?style=social&label=Star)](https://github.com/KwaiVGI/GameFactory) |
| [AgentSociety: Large-scale simulation of LLM-driven generative agents  **(AgentSociety)**](https://arxiv.org/pdf/2502.08691) | ACL 2025, COLM 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/AgentSociety.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/AgentSociety) |
| [EnerVerse-AC: Envisioning Embodied Environments with Action Condition  **(EnerVerse)**](https://arxiv.org/pdf/2505.09723) | 2025                | [![Star](https://img.shields.io/github/stars/AgibotTech/EnerVerse-AC.svg?style=social&label=Star)](https://github.com/AgibotTech/EnerVerse-AC) |
| [GR-3 Technical Report  **(GR3)**](https://arxiv.org/pdf/2507.15493) | 2025                | [![Website](https://img.shields.io/badge/Website-9cf)](https://wayve.ai/thinking/gaia-2/) |
| [Aether: Geometric-Aware Unified World Modeling  **(Aether)**](https://arxiv.org/pdf/2503.18945) | 2025                | [![Star](https://img.shields.io/github/stars/InternRobotics/Aether.svg?style=social&label=Star)](https://github.com/InternRobotics/Aether) |
| [GWM: Towards Scalable Gaussian World Models for Robotic Manipulation  **(GWM)**](https://arxiv.org/pdf/2508.17600) | ICCV 2025           | [![Website](https://img.shields.io/badge/Website-9cf)](https://gaussian-world-model.github.io/) |
| [AirScape: An Aerial Generative World Model with Motion Controllability  **(AirScape)**](https://arxiv.org/pdf/2507.08885) | ACM MM 2025         | [![Website](https://img.shields.io/badge/Website-9cf)](https://embodiedcity.github.io/AirScape/) |
| [RoboScape: Physics-informed Embodied World Model  **(RoboScape)**](https://arxiv.org/pdf/2506.23135) | 2025                | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/RoboScape.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/RoboScape) |
| [DreamGen: Unlocking Generalization in Robot Learning through Video World Models  **(DreamGen)**](https://arxiv.org/pdf/2505.12705) | 2025                | [![Star](https://img.shields.io/github/stars/NVIDIA/GR00T-Dreams.svg?style=social&label=Star)](https://github.com/NVIDIA/GR00T-Dreams) |
| [Matrix-Game: Interactive World Foundation Model  **(Matrix-Game)**](https://arxiv.org/pdf/2506.18701) | 2025                | [![Star](https://img.shields.io/github/stars/SkyworkAI/Matrix-Game.svg?style=social&label=Star)](https://github.com/SkyworkAI/Matrix-Game) |
| [Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation  **(Genie Envisioner)**](https://arxiv.org/abs/2508.05635v1) | 2025                | [![Star](https://img.shields.io/github/stars/AgibotTech/Genie-Envisioner.svg?style=social&label=Star)](https://github.com/AgibotTech/Genie-Envisioner) |

## 3 Implicit Representation of the External World

### 3.1 World Model in Decision Making

| Title | Pub. & Date | Code/Project URL |
|---|---|---|
| [Deep reinforcement learning in a handful of trials using probabilistic dynamics models](https://proceedings.neurips.cc/paper_files/paper/2018/file/3de568f8597b94bda53149c7d7f5958c-Paper.pdf) | NeurIPS 2018 | [![Star](https://img.shields.io/github/stars/kchua/handful-of-trials.svg?style=social&label=Star)](https://github.com/kchua/handful-of-trials) |
| [PWM: Policy Learning with Multi-Task World Models](https://arxiv.org/pdf/2407.02466) | ICLR 2025 | [![Star](https://img.shields.io/github/stars/imgeorgiev/PWM.svg?style=social&label=Star)](https://github.com/imgeorgiev/PWM) |
| [Recurrent world models facilitate policy evolution](https://proceedings.neurips.cc/paper/2018/file/2de5d16682c3c35007e4e92982f1a2ba-Paper.pdf) | NeurIPS 2018 | [![Website](https://img.shields.io/badge/Website-9cf)](https://worldmodels.github.io/) |
| [Dream to control: Learning behaviors by latent imagination](https://arxiv.org/pdf/1912.01603) | ICLR 2020 | [![Star](https://img.shields.io/github/stars/danijar/dreamer.svg?style=social&label=Star)](https://github.com/danijar/dreamer) |
| [Leveraging pre-trained large language models to construct and utilize world models for model-based task planning](https://arxiv.org/pdf/2305.14909) | NeurIPS 2023 | [![Star](https://img.shields.io/github/stars/GuanSuns/LLMs-World-Models-for-Planning.svg?style=social&label=Star)](https://github.com/GuanSuns/LLMs-World-Models-for-Planning) |
| [Mastering atari with discrete world models](https://arxiv.org/pdf/2010.02193) | ICLR 2021 | [![Star](https://img.shields.io/github/stars/danijar/dreamerv2.svg?style=social&label=Star)](https://github.com/danijar/dreamerv2) |
| [Mastering diverse control tasks through world models](https://arxiv.org/pdf/2301.04104) | Nature 2024 | [![Star](https://img.shields.io/github/stars/danijar/dreamerv3.svg?style=social&label=Star)](https://github.com/danijar/dreamerv3) |
| [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://openreview.net/pdf?id=Oxh5CstDJU) | ICLR 2024 | [![Star](https://img.shields.io/github/stars/nicklashansen/tdmpc2.svg?style=social&label=Star)](https://github.com/nicklashansen/tdmpc2) |
| [When to trust your model: Model-based policy optimization](https://arxiv.org/pdf/1906.08253) | NeurIPS 2019 | [![Star](https://img.shields.io/github/stars/jannerm/mbpo.svg?style=social&label=Star)](https://github.com/jannerm/mbpo) |
| [Offline reinforcement learning as one big sequence modeling problem](https://proceedings.neurips.cc/paper/2021/file/099fe6b0b444c23836c4a5d07346082b-Paper.pdf) | NeurIPS 2021 | [![Star](https://img.shields.io/github/stars/jannerm/trajectory-transformer.svg?style=social&label=Star)](https://github.com/jannerm/trajectory-transformer) |
| [Model predictive control](https://link.springer.com/content/pdf/10.1007/978-3-319-24853-0.pdf) | Springer | — |
| [Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees](https://openreview.net/pdf?id=BJe1E2R5KX) | ICLR 2019 | [![Star](https://img.shields.io/github/stars/roosephu/slbo.svg?style=social&label=Star)](https://github.com/roosephu/slbo) |
| [Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning](https://arxiv.org/pdf/1708.02596) | ICRA 2018 | [![Star](https://img.shields.io/github/stars/anagabandi/nn_dynamics.svg?style=social&label=Star)](https://github.com/anagabandi/nn_dynamics) |
| [A game theoretic framework for model based reinforcement learning](https://arxiv.org/pdf/2004.07804) | ICML 2021 | [![Star](https://img.shields.io/github/stars/aravindr93/mjrl.svg?style=social&label=Star)](https://github.com/aravindr93/mjrl/tree/v2) |
| [General agents need world models](https://arxiv.org/pdf/2506.01622) | ICML 2025    | — |
| [Mastering memory tasks with world models](https://arxiv.org/abs/2403.04253) | ICLR 2024 | [![Star](https://img.shields.io/github/stars/chandar-lab/Recall2Imagine.svg?style=social&label=Star)](https://github.com/chandar-lab/Recall2Imagine) |
| [A generalist dynamics model for control](https://arxiv.org/abs/2305.10912) | arXiv 2023 | — |
| [Exploring model-based planning with policy networks](https://openreview.net/pdf?id=H1exf64KwH) | ICLR 2020 | [![Star](https://img.shields.io/github/stars/WilsonWangTHU/POPLIN.svg?style=social&label=Star)](https://github.com/WilsonWangTHU/POPLIN) |
| [A0c: Alpha zero in continuous action space](https://arxiv.org/abs/1805.09613) | arXiv 2018 | [![Star](https://img.shields.io/github/stars/tmoer/a0c.svg?style=social&label=Star)](https://github.com/tmoer/a0c) |
| [Probabilistic adaptation of text-to-video models](https://arxiv.org/pdf/2306.01872v1) | ICLR 2024 | [![Website](https://img.shields.io/badge/Website-9cf)](https://video-adapter.github.io/video-adapter/) |
| [RoboDreamer: Learning Compositional World Models for Robot Imagination](https://openreview.net/pdf?id=kHjOmAUfVe) | ICML 2024 | [![Star](https://img.shields.io/github/stars/rainbow979/robodreamer.svg?style=social&label=Star)](https://github.com/rainbow979/robodreamer) |
| [Discuss before moving: Visual language navigation via multi-expert discussions](https://arxiv.org/pdf/2309.11382) | ICRA 2024 | [![Star](https://img.shields.io/github/stars/LYX0501/DiscussNav.svg?style=social&label=Star)](https://github.com/LYX0501/DiscussNav) |
| [OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and Structured Representation](https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_OVER-NAV_Elevating_Iterative_Vision-and-Language_Navigation_with_Open-Vocabulary_Detection_and_StructurEd_CVPR_2024_paper.pdf) | CVPR 2024 | [![Star](https://img.shields.io/github/stars/uitrbn/OVER-NAV-IVLN-CE.svg?style=social&label=Star)](https://github.com/uitrbn/OVER-NAV-IVLN-CE) |
| [RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation](https://openaccess.thecvf.com/content/CVPR2024//papers/Yang_RILA_Reflective_and_Imaginative_Language_Agent_for_Zero-Shot_Semantic_Audio-Visual_CVPR_2024_paper.pdf) | CVPR 2024 | [![Website](https://img.shields.io/badge/Website-9cf)](https://rila-savn.github.io/RILA/) |
| [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://arxiv.org/pdf/2501.09686) | arXiv 2025 | — |
| [Position: LLMs can't plan, but can help planning in LLM-modulo frameworks](https://arxiv.org/pdf/2402.01817) | ICML 2024 | — |
| [Language models meet world models: Embodied experiences enhance language models](https://proceedings.neurips.cc/paper_files/paper/2023/file/ee6630dcbcff857026e474fc857aa9f0-Paper-Conference.pdf#:~:text=In%20this%20paper%2C%20we%20propose%20a%20new%20paradigm,embodied%20knowledge%20while%20retaining%20their%20general%20language%20capabilities.) | NeurIPS 2023 | [![Star](https://img.shields.io/github/stars/szxiangjn/world-model-for-language-model.svg?style=social&label=Star)](https://github.com/szxiangjn/world-model-for-language-model) |
| [Virtualhome: Simulating household activities via programs](https://openaccess.thecvf.com/content_cvpr_2018/papers/Puig_VirtualHome_Simulating_Household_CVPR_2018_paper.pdf) | CVPR 2018 | [![Star](https://img.shields.io/github/stars/xavierpuigf/virtualhome.svg?style=social&label=Star)](https://github.com/xavierpuigf/virtualhome) |
| [Learning to Model the World with Language](https://arxiv.org/pdf/2308.01399) | ICML 2024 | [![Star](https://img.shields.io/github/stars/jlin816/dynalang.svg?style=social&label=Star)](https://github.com/jlin816/dynalang) |
| [Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency](https://arxiv.org/abs/2309.17382) | ICML 2024 | [![Star](https://img.shields.io/github/stars/agentification/RAFA_code.svg?style=social&label=Star)](https://github.com/agentification/RAFA_code) |
| [Alfworld: Aligning text and embodied environments for interactive learning](https://arxiv.org/pdf/2010.03768) | ICLR 2021 | [![Star](https://img.shields.io/github/stars/alfworld/alfworld.svg?style=social&label=Star)](https://github.com/alfworld/alfworld) |
| [Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents](https://arxiv.org/abs/2411.06559) | EMNLP 2024 | [![Star](https://img.shields.io/github/stars/OSU-NLP-Group/WebDreamer.svg?style=social&label=Star)](https://github.com/OSU-NLP-Group/WebDreamer) |
| [Agent Planning with World Knowledge Model](https://arxiv.org/pdf/2405.14205) | NeurIPS 2024 | [![Star](https://img.shields.io/github/stars/zjunlp/WKM.svg?style=social&label=Star)](https://github.com/zjunlp/WKM) |
| [WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment](https://arxiv.org/pdf/2402.12275) | NeurIPS 2024 | [![Star](https://img.shields.io/github/stars/haotang1995/WorldCoder.svg?style=social&label=Star)](https://github.com/haotang1995/WorldCoder) |
| [Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation](https://arxiv.org/pdf/2410.13232) | ICLR 2025 | [![Star](https://img.shields.io/github/stars/kyle8581/WMA-Agents.svg?style=social&label=Star)](https://github.com/kyle8581/WMA-Agents) |

### 3.2 World Knowledge Learned by Models

| Title | Pub. & Date | Code / Project URL |
|---|---|---|
| [Does the chimpanzee have a theory of mind?](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/1E96B02CD9850016B7C93BC6D2FEF1D0/S0140525X00076512a.pdf/does-the-chimpanzee-have-a-theory-of-mind.pdf) | Behav. & Brain Sci. 1978 | — |
| [GPT4GEO: How a Language Model Sees the World’s Geography](https://arxiv.org/pdf/2306.00020) | NeurIPS  2023 | [![Star](https://img.shields.io/github/stars/jonathan-roberts1/GPT4GEO.svg?style=social&label=Star)](https://github.com/jonathan-roberts1/GPT4GEO) |
| [LLMs achieve adult human performance on higher-order theory of mind tasks](https://arxiv.org/pdf/2405.18870) | arXiv 2024 | — |
| [COKE: A cognitive knowledge graph for machine theory of mind](https://aclanthology.org/2024.acl-long.848.pdf) | ACL 2024 | [![Star](https://img.shields.io/github/stars/jincenziwu/COKE.svg?style=social&label=Star)](https://github.com/jincenziwu/COKE) |
| [Think Twice: Perspective-Taking Improves LLM Theory-of-Mind](https://arxiv.org/pdf/2311.10227) | ACL 2024 | [![Star](https://img.shields.io/github/stars/shawnsihyunlee/simulatedtom.svg?style=social&label=Star)](https://github.com/shawnsihyunlee/simulatedtom) |
| [Language Models Represent Space and Time](https://arxiv.org/pdf/2310.02207) | ICLR 2024 | [![Star](https://img.shields.io/github/stars/wesg52/world-models.svg?style=social&label=Star)](https://github.com/wesg52/world-models) |
| [GeoLLM: Extracting Geospatial Knowledge from Large Language Models](https://arxiv.org/pdf/2310.06213) | ICLR 2024 | [![Star](https://img.shields.io/github/stars/rohinmanvi/GeoLLM.svg?style=social&label=Star)](https://github.com/rohinmanvi/GeoLLM) |
| [Large language models are geographically biased](https://arxiv.org/pdf/2402.02680) | ICML 2024 | [![Star](https://img.shields.io/github/stars/rohinmanvi/GeoLLM.svg?style=social&label=Star)](https://github.com/rohinmanvi/GeoLLM) |
| [Emergent Representations of Program Semantics in Language Models Trained on Programs](https://openreview.net/pdf?id=zRrzSLwNHQ) | ICML 2024 | [![Star](https://img.shields.io/github/stars/charlesjin/emergent-semantics.svg?style=social&label=Star)](https://github.com/charlesjin/emergent-semantics) |
| [BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages](https://arxiv.org/pdf/2406.09948) | NeurIPS 2024 | [![Star](https://img.shields.io/github/stars/nlee0212/BLEnD.svg?style=social&label=Star)](https://github.com/nlee0212/BLEnD) |
| [SafeWorld: Geo-Diverse Safety Alignment](https://arxiv.org/pdf/2412.06483) | NeurIPS 2024 | [![Star](https://img.shields.io/github/stars/PlusLabNLP/SafeWorld.svg?style=social&label=Star)](https://github.com/PlusLabNLP/SafeWorld) |
| [EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas](https://openreview.net/forum?id=8aAaYEwNR4) | NeurIPS 2024 | [![Star](https://img.shields.io/github/stars/AIRI-Institute/EAI-Framework.svg?style=social&label=Star)](https://github.com/AIRI-Institute/EAI-Framework) |
| [Testing theory of mind in large language models and humans](https://www.nature.com/articles/s41562-024-01882-z) | Nature Human Behaviour 2024 | [Website](https://osf.io/j3vhq) |
| [Automated construction of cognitive maps with visual predictive coding](https://www.nature.com/articles/s42256-024-00863-1) | Nature Machine Intelligence 2024 | [![Star](https://img.shields.io/github/stars/jgornet/predictive-coding-recovers-maps.svg?style=social&label=Star)](https://github.com/jgornet/predictive-coding-recovers-maps) |
| [Evaluating Large Language Models in Theory of Mind Tasks](https://www.pnas.org/doi/10.1073/pnas.2405460121) | PNAS 2024 | [Website](https://osf.io/csdhb/) |
| [Elements of World Knowledge (EWOK)](https://arxiv.org/pdf/2405.09605) | Transactions of the ACL 2025 | [Website](https://ewok-core.github.io/) |
| [The Geometry of Concepts: Sparse Autoencoder Feature Structure](https://arxiv.org/abs/2410.19750) | Entropy 2025 | [![Star](https://img.shields.io/github/stars/ejmichaud/feature-geometry.svg?style=social&label=Star)](https://github.com/ejmichaud/feature-geometry) |
| [AgentMove: A large language model based agentic framework for zero-shot next location prediction](https://arxiv.org/abs/2408.13986) | NAACL 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/AgentMove.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/AgentMove) |
| [CityGPT: Empowering Urban Spatial Cognition of Large Language Models](https://arxiv.org/abs/2406.13948) | KDD 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/CityGPT.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/CityGPT) |
| [CityBench: Evaluating the Capabilities of Large Language Model as World Model](https://arxiv.org/abs/2406.13945) | KDD 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/CityBench.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/CityBench) |
| [LocalGPT: Benchmarking and Advancing Large Language Models for Local Life Services](https://www.arxiv.org/abs/2506.02720) | KDD 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/LocalEval.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/LocalEval) |
| [UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence](https://arxiv.org/abs/2506.23219) | ICCV 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/UrbanLLaVA.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/UrbanLLaVA) |
| [Open-Set Living Need Prediction with Large Language Models](https://arxiv.org/abs/2506.02713) | ACL 2025 Findings | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/PIGEON.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/PIGEON) |
| [Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation](https://arxiv.org/abs/2506.21876) | ACL 2025 Findings | [Website](https://wm-abench.maitrix.org/) |
| [Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning](https://arxiv.org/abs/2507.19586) | EMNLP 2025 Findings |[![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/GeospatialHallucination.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/GeospatialHallucination) |
| [GPS as a Control Signal for Image Generation](https://arxiv.org/abs/2501.12390) | CVPR 2025 | [![Star](https://img.shields.io/github/stars/cfeng16/GPS2Pix.svg?style=social&label=Star)](https://github.com/cfeng16/GPS2Pix) |
| [All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages](https://arxiv.org/abs/2411.16508) | CVPR 2025 | [Website](https://mbzuai-oryx.github.io/ALM-Bench/) |
| [Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models](https://arxiv.org/abs/2502.08636) | CVPR 2025 | [![Star](https://img.shields.io/github/stars/XingruiWang/Spatial457.svg?style=social&label=Star)](https://github.com/XingruiWang/Spatial457) |
| [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://arxiv.org/abs/2412.14171) | CVPR 2025 | [Website](https://vision-x-nyu.github.io/thinking-in-space.github.io/) |
| [A Survey of Large Language Model-Powered Spatial Intelligence Across Scales](https://arxiv.org/abs/2504.09848) | arXiv 2025 | — |
| [AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario](https://arxiv.org/pdf/2506.16898) | arXiv 2025 | — |
| [Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models](https://arxiv.org/pdf/2506.14674) | arXiv 2025 | — |

## 4 Future Prediction of the Physical World

### 4.1 World Model as Video Generation

| Title                                                        | Pub. & Date      | Code / Project URL                                           |
| ------------------------------------------------------------ | ---------------- | ------------------------------------------------------------ |
| [Video generation models as world simulators](https://openai.com/research/video-generation-models-as-world-simulators) | OpenAI Blog 2024 | —                                                            |
| [Sora: Creating video from text](https://openai.com/sora)    | OpenAI 2024      | —                                                            |
| [Is Sora a world simulator? A comprehensive survey on general world models and beyond](https://arxiv.org/pdf/2405.03520) | arXiv 2024       | [![Star](https://img.shields.io/github/stars/GigaAI-research/General-World-Models-Survey.svg?style=social&label=Star)](https://github.com/GigaAI-research/General-World-Models-Survey) |
| [Sora as an AGI world model? A complete survey on text-to-video generation](https://arxiv.org/pdf/2403.05131) | arXiv 2024       | —                                                            |
| [How Far is Video Generation from World Model: A Physical Law Perspective](https://arxiv.org/pdf/2411.02385) | ICML 2025        | [![Star](https://img.shields.io/github/stars/phyworld/phyworld.svg?style=social&label=Star)](https://github.com/phyworld/phyworld) |
| [Do generative video models learn physical principles from watching videos?](https://arxiv.org/pdf/2501.09038) | arXiv 2025       | [![Star](https://img.shields.io/github/stars/google-deepmind/physics-IQ-benchmark.svg?style=social&label=Star)](https://github.com/google-deepmind/physics-IQ-benchmark) |
| [Genesis: A Generative and Universal Physics Engine for Robotics and Beyond](https://arxiv.org/pdf/2405.18556) | ICML 2024        | [![Star](https://img.shields.io/github/stars/Genesis-Embodied-AI/Genesis.svg?style=social&label=Star)](https://github.com/Genesis-Embodied-AI/Genesis) |
| [PhysGen: Rigid-body physics-grounded image-to-video generation](https://arxiv.org/pdf/2409.18964) | ECCV 2024        | [![Star](https://img.shields.io/github/stars/stevenlsw/physgen.svg?style=social&label=Star)](https://github.com/stevenlsw/physgen) |
| [NUWA-XL: Diffusion over Diffusion for Extremely Long Video Generation](https://arxiv.org/pdf/2303.12346) | ACL 2023         | [Website](https://msra-nuwa.azurewebsites.net/#/)            |
| [OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving](https://arxiv.org/pdf/2311.16038) | ECCV 2024        | [![Star](https://img.shields.io/github/stars/wzzheng/OccWorld.svg?style=social&label=Star)](https://github.com/wzzheng/OccWorld) |
| [OccSora: 4D Occupancy Generation Models as World Simulators](https://openreview.net/pdf?id=XIFnghzusY) | ICLR 2025        | [![Star](https://img.shields.io/github/stars/wzzheng/OccSora.svg?style=social&label=Star)](https://github.com/wzzheng/OccSora) |
| [World model on million-length video and language with ring-attention](https://arxiv.org/pdf/2402.08268) | ICLR 2025        | [![Star](https://img.shields.io/github/stars/LargeWorldModel/lwm.svg?style=social&label=Star)](https://github.com/LargeWorldModel/lwm) |
| [GAIA-1: A generative world model for autonomous driving](https://arxiv.org/pdf/2309.17080) | arXiv 2023       | [Website](https://anthonyhu.github.io/gaia1)                 |
| [DriveDreamer: Towards real-world-driven world models](https://arxiv.org/pdf/2309.09777) | ECCV 2024        | [![Star](https://img.shields.io/github/stars/JeffWang987/DriveDreamer.svg?style=social&label=Star)](https://github.com/JeffWang987/DriveDreamer) |
| [DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation](https://arxiv.org/pad/2403.06845) | AAAI 2025        | [![Star](https://img.shields.io/github/stars/f1yfisher/DriveDreamer2.svg?style=social&label=Star)](https://github.com/f1yfisher/DriveDreamer2) |
| [Driving into the Future: Multiview Visual Forecasting and Planning with World Model](https://arxiv.org/pdf/2311.17918) | CVPR 2024        | [![Star](https://img.shields.io/github/stars/BraveGroup/Drive-WM.svg?style=social&label=Star)](https://github.com/BraveGroup/Drive-WM) |
| [Vista: A Generalizable Driving World Model with High Fidelity](https://arxiv.org/pdf/2405.17398) | NeurIPS 2024     | [![Star](https://img.shields.io/github/stars/OpenDriveLab/Vista.svg?style=social&label=Star)](https://github.com/OpenDriveLab/Vista) |
| [WorldDreamer: Towards general world models for video generation](https://arxiv.org/abs/2401.09985) | arXiv 2024       | [![Star](https://img.shields.io/github/stars/JeffWang987/WorldDreamer.svg?style=social&label=Star)](https://github.com/JeffWang987/WorldDreamer) |
| [WorldGPT: a Sora-inspired video AI agent](https://arxiv.org/abs/2403.07944) | arXiv 2024       | —                                                            |

### 4.2 World Model as Embodied Environment

| Title | Pub. & Date | Code / Project URL |
|---|---|---|
| [Holodeck: Language guided generation of 3d embodied ai environments](https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Holodeck_Language_Guided_Generation_of_3D_Embodied_AI_Environments_CVPR_2024_paper.pdf) | CVPR 2024 | [![Star](https://img.shields.io/github/stars/allenai/Holodeck.svg?style=social&label=Star)](https://github.com/allenai/Holodeck) |
| [GRUtopia: Dream General Robots in a City at Scale](https://arxiv.org/abs/2407.10943) | arXiv 2024 | [![Star](https://img.shields.io/github/stars/InternRobotics/InternUtopia.svg?style=social&label=Star)](https://github.com/InternRobotics/InternUtopia) |
| [Anyhome: Open-vocabulary generation of structured and textured 3d homes](https://arxiv.org/abs/2312.06644) | ECCV 2024 | [![Star](https://img.shields.io/github/stars/FreddieRao/anyhome_github.svg?style=social&label=Star)](https://github.com/FreddieRao/anyhome_github) |
| [LEGENT: Open Platform for Embodied Agents](https://arxiv.org/abs/2404.18243) | arXiv 2024 | [![Star](https://img.shields.io/github/stars/thunlp/LEGENT.svg?style=social&label=Star)](https://github.com/thunlp/LEGENT) |
| [UrbanWorld: An Urban World Model for 3D City Generation](https://arxiv.org/abs/2407.11965) | arXiv 2024 | [![Star](https://img.shields.io/github/stars/Urban-World/UrbanWorld.svg?style=social&label=Star)](https://github.com/Urban-World/UrbanWorld) |
| [MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility](https://arxiv.org/pdf/2407.08725) | ICLR 2025 | [![Star](https://img.shields.io/github/stars/metadriverse/metaurban.svg?style=social&label=Star)](https://github.com/metadriverse/metaurban) |
| [Minedojo: Building open-ended embodied agents with internet-scale knowledge](https://arxiv.org/abs/2206.08853) | NeurIPS 2022 | [![Star](https://img.shields.io/github/stars/MineDojo/MineDojo.svg?style=social&label=Star)](https://github.com/MineDojo/MineDojo) |
| [UniSim: Learning Interactive Real-World Simulators](https://openreview.net/pdf?id=sFyTZEqmUY) | ICLR 2024 | [![Website](https://img.shields.io/badge/Website-9cf)](https://universal-simulator.github.io/unisim/) |
| [EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment](https://arxiv.org/abs/2410.09604) | arXiv 2024 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/EmbodiedCity.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/EmbodiedCity) |
| [Empowering World Models with Reflection for Embodied Video Prediction](https://arxiv.org/abs/2410.15461) | ICML 2025 | — |
| [Streetscapes: Large-scale consistent street view generation using autoregressive video diffusion](https://arxiv.org/abs/2406.03578) | SIGGRAPH 2024 | — |
| [AVID: Adapting Video Diffusion Models to World Models](https://arxiv.org/abs/2410.12822) | arXiv 2024 | [![Star](https://img.shields.io/github/stars/microsoft/causica.svg?style=social&label=Star)](https://github.com/microsoft/causica/tree/main/research_experiments/avid) |
| [Pandora: Towards General World Model with Natural Language Actions and Video States](https://arxiv.org/abs/2406.09455) | arXiv 2024 | [![Star](https://img.shields.io/github/stars/maitrix-org/Pandora.svg?style=social&label=Star)](https://github.com/maitrix-org/Pandora) |
| [RoboScape: Physics-informed Embodied World Model](https://arxiv.org/abs/2506.23135) | arXiv 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/RoboScape.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/RoboScape) |
| [TesserAct: Learning 4D Embodied World Models](https://arxiv.org/abs/2504.20995) | arXiv 2025 | [![Star](https://img.shields.io/github/stars/UMass-Embodied-AGI/TesserAct.svg?style=social&label=Star)](https://github.com/UMass-Embodied-AGI/TesserAct) |

## 5 Applications of World Models

### 5.1 Game Intelligence

| Title                                                        | Pub. & Date | Code / Project URL                                           |
| ------------------------------------------------------------ | ----------- | ------------------------------------------------------------ |
| [World and Human Action Models towards Gameplay Ideation](https://www.nature.com/articles/s41586-025-08600-3.pdf) | Nature 2025 | —                                                            |
| [GameFactory: Creating New Games with Generative Interactive Videos](https://arxiv.org/pdf/2501.08325) | ICCV 2025   | [![Star](https://img.shields.io/github/stars/KwaiVGI/GameFactory.svg?style=social&label=Star)](https://github.com/KwaiVGI/GameFactory) |
| [Unbounded: A Generative Infinite Game of Character Life Simulation](https://arxiv.org/pdf/2410.18975) | CVPR 2025   | [Website](https://generative-infinite-game.github.io/)       |
| [GameGen-𝕏: Interactive Open-world Game Video Generation](https://arxiv.org/pdf/2411.00769) | ICLR 2025   | [![Star](https://img.shields.io/github/stars/GameGen-X/GameGen-X.svg?style=social&label=Star)](https://github.com/GameGen-X/GameGen-X) |
| [Diffusion Models Are Real-Time Game Engines](https://arxiv.org/pdf/2408.14837?) | ICLR 2025   | [Website](https://gamengen.github.io/)                       |
| [Exploration-Driven Generative Interactive Environments](https://arxiv.org/pdf/2506.09985) | ICLR 2025   | [![Star](https://img.shields.io/github/stars/facebookresearch/vjepa2.svg?style=social&label=Star)](https://github.com/facebookresearch/vjepa2) |
| [Matrix-Game: Interactive World Foundation Model](https://arxiv.org/pdf/2506.18701) | arXiv 2025  | [![Star](https://img.shields.io/github/stars/SkyworkAI/Matrix-Game.svg?style=social&label=Star)](https://github.com/SkyworkAI/Matrix-Game) |
| [Mineworld: a Real-time and Open-source Interactive World Model on Minecraft](https://arxiv.org/pdf/2504.08388?) | arXiv 2025  | [![Star](https://img.shields.io/github/stars/microsoft/MineWorld.svg?style=social&label=Star)](https://github.com/microsoft/MineWorld) |
| [Model as a Game: On Numerical and Spatial Consistency for Generative Games](https://arxiv.org/pdf/2503.21172?) | arXiv 2025  | —                                                            |

### 5.2 Embodied Intelligence

| Title                                                        | Pub. & Date  | Code / Project URL                                           |
| ------------------------------------------------------------ | ------------ | ------------------------------------------------------------ |
| [OpenEQA: Embodied Question Answering in the Era of Foundation Models](https://open-eqa.github.io/assets/pdfs/paper.pdf) | CVPR 2024    | [![Star](https://img.shields.io/github/stars/facebookresearch/open-eqa.svg?style=social&label=Star)](https://github.com/facebookresearch/open-eqa) |
| [iVideoGPT: Interactive VideoGPTs are Scalable World Models](https://arxiv.org/abs/2405.15223) | NeurIPS 2024 | [![Star](https://img.shields.io/github/stars/thuml/iVideoGPT.svg?style=social&label=Star)](https://github.com/thuml/iVideoGPT) |
| [IRASim: A Fine-Grained World Model for Robot Manipulation](https://arxiv.org/abs/2406.14540) | ICCV 2025    | [![Star](https://img.shields.io/github/stars/bytedance/IRASim.svg?style=social&label=Star)](https://github.com/bytedance/IRASim) |
| [RoboScape: Physics-informed Embodied World Model](https://arxiv.org/abs/2506.23135) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/RoboScape.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/RoboScape) |
| [TesserAct: Learning 4D Embodied World Models](https://arxiv.org/abs/2504.20995) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/UMass-Embodied-AGI/TesserAct.svg?style=social&label=Star)](https://github.com/UMass-Embodied-AGI/TesserAct) |
| [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://arxiv.org/abs/2506.09985) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/facebookresearch/vjepa2.svg?style=social&label=Star)](https://github.com/facebookresearch/vjepa2) |
| [Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations](https://arxiv.org/abs/2412.14803) | ICML 2025    | [![Star](https://img.shields.io/github/stars/roboterax/video-prediction-policy.svg?style=social&label=Star)](https://github.com/roboterax/video-prediction-policy) |
| [DreamGen: Unlocking Generalization in Robot Learning through Video World Models](https://arxiv.org/abs/2505.12705) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/NVIDIA/GR00T-Dreams.svg?style=social&label=Star)](https://github.com/NVIDIA/GR00T-Dreams) |
| [EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation](https://arxiv.org/abs/2501.01895) | arXiv 2025   | [Website](https://sites.google.com/view/enerverse)           |
| [EnerVerse-AC: Envisioning Embodied Environments with Action Condition](https://arxiv.org/abs/2505.09723) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/AgibotTech/EnerVerse-AC.svg?style=social&label=Star)](https://github.com/AgibotTech/EnerVerse-AC) |
| [Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation](https://arxiv.org/abs/2508.05635v1) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/AgibotTech/Genie-Envisioner.svg?style=social&label=Star)](https://github.com/AgibotTech/Genie-Envisioner) |
| [Vidar: Embodied Video Diffusion Model for Generalist Bimanual Manipulation](https://arxiv.org/abs/2507.12898) | arXiv 2025   | —                                                            |
| [WorldVLA: Towards Autoregressive Action World Model](https://arxiv.org/abs/2506.21539) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/alibaba-damo-academy/WorldVLA.svg?style=social&label=Star)](https://github.com/alibaba-damo-academy/WorldVLA) |
| [ManiGaussian++: General Robotic Bimanual Manipulation with Hierarchical Gaussian World Model](https://arxiv.org/pdf/2506.19842) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/April-Yz/ManiGaussian_Bimanual.svg?style=social&label=Star)](https://github.com/April-Yz/ManiGaussian_Bimanual) |
| [ORV: 4D Occupancy-centric Robot Video Generation](https://arxiv.org/abs/2506.03079) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/OrangeSodahub/ORV.svg?style=social&label=Star)](https://github.com/OrangeSodahub/ORV) |
| [GWM: Towards Scalable Gaussian World Models for Robotic Manipulation](https://arxiv.org/pdf/2508.17600) | ICCV 2025    | [Website](https://gaussian-world-model.github.io/)           |
| [WorldEval: World Model as Real-World Robot Policies Evaluator](https://arxiv.org/abs/2505.19017) | arXiv 2025   | [![Star](https://img.shields.io/github/stars/liyaxuanliyaxuan/Worldeval.svg?style=social&label=Star)](https://github.com/liyaxuanliyaxuan/Worldeval) |

### 5.3 Urban Intelligence

#### Autonomous Driving

| Title | Pub. & Date | Code / Project URL |
|---|---|---|
| [Video generation models as world simulators](https://openai.com/research/video-generation-models-as-world-simulators) | OpenAI Research (2024) | [Website](https://openai.com/research/video-generation-models-as-world-simulators) |
| [GPT-4 technical report](https://arxiv.org/abs/2303.08774) | arXiv 2023 | — |
| [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) | NeurIPS 2023 | [![Star](https://img.shields.io/github/stars/haotian-liu/LLaVA.svg?style=social&label=Star)](https://github.com/haotian-liu/LLaVA) |
| [World models for autonomous driving: An initial survey](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522953) | IEEE T-IV 2024 | — |
| [Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research](https://arxiv.org/pdf/2310.17918) | arXiv 2023 | [![Star](https://img.shields.io/github/stars/waymo-research/waymax.svg?style=social&label=Star)](https://github.com/waymo-research/waymax) |
| [Planning-oriented autonomous driving](https://arxiv.org/abs/2212.10156) | CVPR 2023 | [![Star](https://img.shields.io/github/stars/OpenDriveLab/UniAD.svg?style=social&label=Star)](https://github.com/OpenDriveLab/UniAD) |
| [A survey on trajectory-prediction methods for autonomous driving](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9779556) | IEEE T-IV 2022 | — |
| [BEVFormer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers](https://arxiv.org/abs/2203.17270) | ECCV 2022 | [![Star](https://img.shields.io/github/stars/fundamentalvision/BEVFormer.svg?style=social&label=Star)](https://github.com/fundamentalvision/BEVFormer) |
| [Transfusion: Robust lidar-camera fusion for 3D object detection with transformers](https://openaccess.thecvf.com/content/CVPR2022/papers/Bai_TransFusion_Robust_LiDAR-Camera_Fusion_for_3D_Object_Detection_With_Transformers_CVPR_2022_paper.pdf) | CVPR 2022 | [![Star](https://img.shields.io/github/stars/XuyangBai/TransFusion.svg?style=social&label=Star)](https://github.com/XuyangBai/TransFusion) |
| [YOLOP: You only look once for panoptic driving perception](https://link.springer.com/article/10.1007/s11633-022-1339-y) | MIR 2022 | [![Star](https://img.shields.io/github/stars/hustvl/YOLOP.svg?style=social&label=Star)](https://github.com/hustvl/YOLOP) |
| [Wayformer: Motion forecasting via simple & efficient attention networks](https://arxiv.org/abs/2207.05844) | ICRA 2023 | — |
| [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://papers.nips.cc/paper_files/paper/2022/file/2ab47c960bfee4f86dfc362f26ad066a-Paper-Conference.pdf) | NeurIPS 2022 | [![Star](https://img.shields.io/github/stars/sshaoshuai/MTR.svg?style=social&label=Star)](https://github.com/sshaoshuai/MTR) |
| [Query-Centric Trajectory Prediction](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf) | CVPR 2023 | [![Star](https://img.shields.io/github/stars/ZikangZhou/QCNet.svg?style=social&label=Star)](https://github.com/ZikangZhou/QCNet) |
| [HPTR: Real-time motion prediction via heterogeneous polyline transformer with relative pose encoding](https://arxiv.org/abs/2310.12970) | NeurIPS 2023 | [![Star](https://img.shields.io/github/stars/zhejz/HPTR.svg?style=social&label=Star)](https://github.com/zhejz/HPTR) |
| [MotionDiffuser: Controllable multi-agent motion prediction using diffusion](https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_MotionDiffuser_Controllable_Multi-Agent_Motion_Prediction_Using_Diffusion_CVPR_2023_paper.pdf) | CVPR 2023 | — |
| [Tokenize the world into object-level knowledge to address long-tail events in autonomous driving](https://arxiv.org/abs/2407.00959) | arXiv 2024 | — |
| [GAIA-1: A generative world model for autonomous driving](https://arxiv.org/pdf/2309.17080) | arXiv 2023             | [Website](https://anthonyhu.github.io/gaia1)                 |
| [DriveDreamer: Towards real-world-driven world models for autonomous driving](https://arxiv.org/abs/2309.09777) | ECCV 2024 | [![Star](https://img.shields.io/github/stars/JeffWang987/DriveDreamer.svg?style=social&label=Star)](https://github.com/JeffWang987/DriveDreamer) |
| [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://arxiv.org/abs/2311.17918) | CVPR 2024 | [![Star](https://img.shields.io/github/stars/BraveGroup/Drive-WM.svg?style=social&label=Star)](https://github.com/BraveGroup/Drive-WM) |
| [OccWorld: Learning a 3D occupancy world model for autonomous driving](https://arxiv.org/abs/2311.16038) | ECCV 2024 | [![Star](https://img.shields.io/github/stars/wzzheng/OccWorld.svg?style=social&label=Star)](https://github.com/wzzheng/OccWorld) |
| [OccSora: 4D occupancy generation models as world simulators for autonomous driving](https://arxiv.org/abs/2405.20337) | arXiv 2024 | [![Star](https://img.shields.io/github/stars/wzzheng/OccSora.svg?style=social&label=Star)](https://github.com/wzzheng/OccSora) |
| [Vista: A generalizable driving world model with high fidelity and versatile controllability](https://arxiv.org/abs/2405.17398) | NeurIPS 2024 | [![Star](https://img.shields.io/github/stars/OpenDriveLab/Vista.svg?style=social&label=Star)](https://github.com/OpenDriveLab/Vista) |
| [Copilot4D: Learning unsupervised world models for autonomous driving via discrete diffusion](https://arxiv.org/abs/2311.01017) | ICLR 2024 | — |
| [MUVO: A multimodal generative world model for autonomous driving with geometric representations](https://arxiv.org/abs/2311.11762) | IEEE T-IV 2025 | [![Star](https://img.shields.io/github/stars/fzi-forschungszentrum-informatik/muvo.svg?style=social&label=Star)](https://github.com/fzi-forschungszentrum-informatik/muvo) |
| [UniWorld: Autonomous driving pre-training via world models](https://arxiv.org/abs/2308.07234) | arXiv 2023 | [![Star](https://img.shields.io/github/stars/whuhxb/UniWorld.svg?style=social&label=Star)](https://github.com/whuhxb/UniWorld) |
| [MetaUrban: A simulation platform for embodied AI in urban spaces](https://arxiv.org/abs/2407.08725) | ICLR 2025 | [![Star](https://img.shields.io/github/stars/metadriverse/MetaUrban.svg?style=social&label=Star)](https://github.com/metadriverse/MetaUrban) |
| [UrbanWorld: An urban world model for 3D city generation](https://arxiv.org/abs/2407.11965) | arXiv 2024 | [![Star](https://img.shields.io/github/stars/Urban-World/UrbanWorld.svg?style=social&label=Star)](https://github.com/Urban-World/UrbanWorld) |
| [Streetscapes: Large-scale consistent street view generation using autoregressive video diffusion](https://arxiv.org/pdf/2407.13759) | SIGGRAPH 2024 | [Website](https://arxiv.org/pdf/2407.13759) |

#### Autonomous Logistics & Urban Analytics

| Title | Pub. & Date | Code / Project URL |
|---|---|---|
| [Navigation World Models](https://arxiv.org/abs/2412.03572) | CVPR 2025 | [![Star](https://img.shields.io/github/stars/facebookresearch/nwm.svg?style=social&label=Star)](https://github.com/facebookresearch/nwm) |
| [Towards Autonomous Micromobility through Scalable Urban Simulation](https://arxiv.org/abs/2505.00690) | CVPR 2025 | [![Star](https://img.shields.io/github/stars/metadriverse/urban-sim.svg?style=social&label=Star)](https://github.com/metadriverse/urban-sim) |
| [Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigation](https://arxiv.org/abs/2501.06693) | CVPR 2025 | [![Star](https://img.shields.io/github/stars/Vid2Sim/Vid2Sim.svg?style=social&label=Star)](https://github.com/Vid2Sim/Vid2Sim) |
| [CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos](https://arxiv.org/abs/2411.17820) | CVPR 2025 | [![Star](https://img.shields.io/github/stars/ai4ce/CityWalker.svg?style=social&label=Star)](https://github.com/ai4ce/CityWalker) |
| [AirScape: An Aerial Generative World Model with Motion Controllability  **(AirScape)**](https://arxiv.org/pdf/2507.08885) | ACM MM 2025 | [![Website](https://img.shields.io/badge/Website-9cf)](https://embodiedcity.github.io/AirScape/) |
| [CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory](https://arxiv.org/abs/2505.05622) | ACL 2025 | [![Star](https://img.shields.io/github/stars/EmbodiedCity/CityNavAgent.code.svg?style=social&label=Star)](https://github.com/EmbodiedCity/CityNavAgent.code) |
| [CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space](https://arxiv.org/abs/2502.12532) | EMNLP 2025 | [![Star](https://img.shields.io/github/stars/BiluYong/CityEQA.svg?style=social&label=Star)](https://github.com/BiluYong/CityEQA) |
| [UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces](https://arxiv.org/abs/2503.06157) | ACL 2025 | [![Star](https://img.shields.io/github/stars/EmbodiedCity/UrbanVideo-Bench.code.svg?style=social&label=Star)](https://github.com/EmbodiedCity/UrbanVideo-Bench.code) |
| [GeoLLM: Extracting Geospatial Knowledge from Large Language Models](https://arxiv.org/abs/2310.06213) | ICLR 2024 | [![Star](https://img.shields.io/github/stars/rohinmanvi/GeoLLM.svg?style=social&label=Star)](https://github.com/rohinmanvi/GeoLLM) |
| [CityGPT: Empowering Urban Spatial Cognition of Large Language Models](https://arxiv.org/abs/2406.13948) | KDD 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/CityGPT.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/CityGPT) |
| [UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence](https://arxiv.org/abs/2506.23219) | ICCV 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/UrbanLLaVA.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/UrbanLLaVA) |
| [GPS as a Control Signal for Image Generation](https://arxiv.org/abs/2501.12390) | CVPR 2025 | [![Star](https://img.shields.io/github/stars/cfeng16/GPS2Pix.svg?style=social&label=Star)](https://github.com/cfeng16/GPS2Pix) |
| [AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario](https://arxiv.org/abs/2506.16898) | arXiv 2025 | — |
| [AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction](https://aclanthology.org/2025.naacl-long.61/) | NAACL 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/AgentMove.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/AgentMove) |
| [CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation](https://arxiv.org/abs/2506.13599) | arXiv 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/CAMS.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/CAMS) |
| [Open-Set Living Need Prediction with Large Language Models](https://arxiv.org/abs/2506.02713) | ACL 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/PIGEON.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/PIGEON) |

### 5.4 Societal Intelligence

| Title                                                                                                                                   | Pub. & Date | Code / Project URL                                              |
| --------------------------------------------------------------------------------------------------------------------------------------- | ----------- | --------------------------------------------------------------- |
| [AgentSociety: Large-scale simulation of LLM-driven generative agents](https://arxiv.org/abs/2502.08691)                                | ACL 2025, COLM 2025 | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/AgentSociety.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/AgentSociety)                    |
| [GenSim: A General Social Simulation Platform with Large Language Model based Agents](https://aclanthology.org/2025.naacl-demo.15/)             | NAACL 2025  | [![Star](https://img.shields.io/github/stars/TangJiakai/GenSim.svg?style=social&label=Star)](https://github.com/TangJiakai/GenSim)   |
| [Simulating Human-like Daily Activities with Desire-driven Autonomy](https://arxiv.org/abs/2412.06435)             | ICLR 2025  | [![Star](https://img.shields.io/github/stars/zfw1226/D2A.svg?style=social&label=Star)](https://github.com/zfw1226/D2A)   |
| [EconAgent: Large language model-empowered agents for simulating macroeconomic activities](https://aclanthology.org/2024.acl-long.829/) | ACL 2024    | [![Star](https://img.shields.io/github/stars/tsinghua-fib-lab/ACL24-EconAgent.svg?style=social&label=Star)](https://github.com/tsinghua-fib-lab/ACL24-EconAgent)  |
| [Agent-Pro: Learning to evolve via policy-level reflection and optimization](https://aclanthology.org/2024.acl-long.292/)                          | ACL 2024  | [![Star](https://img.shields.io/github/stars/zwq2018/Agent-Pro.svg?style=social&label=Star)](https://github.com/zwq2018/Agent-Pro)                     |
| [Exploring collaboration mechanisms for LLM agents: A social psychology view](https://aclanthology.org/2024.acl-long.782/)                         | ACL 2024  | [![Star](https://img.shields.io/github/stars/zjunlp/MachineSoM.svg?style=social&label=Star)](https://github.com/zjunlp/MachineSoM)                    |
| [Cooperate or Collapse: Emergence of sustainability behaviors in a society of LLM agents](https://neurips.cc/virtual/2024/poster/96895)             | NeurIPS 2024  | [![Star](https://img.shields.io/github/stars/giorgiopiatti/GovSim.svg?style=social&label=Star)](https://github.com/giorgiopiatti/GovSim)  |
| [SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series](https://iclr.cc/virtual/2024/poster/17662)             | ICLR 2024  | [![Star](https://img.shields.io/github/stars/chengjunyan1/SocioDojo.svg?style=social&label=Star)](https://github.com/chengjunyan1/SocioDojo)   |
| [SRAP-Agent: Simulating and optimizing scarce resource allocation policy with LLM-based agent](https://aclanthology.org/2024.findings-emnlp.15/)        | EMNLP 2024  | [![Star](https://img.shields.io/github/stars/Ji-Cather/SRAPAgent_Framework.svg?style=social&label=Star)](https://github.com/Ji-Cather/SRAPAgent_Framework)                     |
| [Generative agents: Interactive simulacra of human behavior](https://dl.acm.org/doi/10.1145/3586183.3606763)                            | UIST 2023   | [![Star](https://img.shields.io/github/stars/joonspk-research/generative_agents.svg?style=social&label=Star)](https://github.com/joonspk-research/generative_agents) |
| [SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users](https://arxiv.org/abs/2504.10157)             | arXiv 2025  | [![Star](https://img.shields.io/github/stars/FudanDISC/SocioVerse.svg?style=social&label=Star)](https://github.com/FudanDISC/SocioVerse)   |
| [YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models](https://arxiv.org/abs/2505.07581)             | arXiv 2025  | [![Star](https://img.shields.io/github/stars/RUC-GSAI/YuLan-OneSim.svg?style=social&label=Star)](https://github.com/RUC-GSAI/YuLan-OneSim)   |
| [OASIS: Open Agent Social Interaction Simulations with One Million Agents](https://arxiv.org/abs/2411.11581)             | arXiv 2024  | [![Star](https://img.shields.io/github/stars/camel-ai/oasis.svg?style=social&label=Star)](https://github.com/camel-ai/oasis)   |
| [Project Sid: Many-agent simulations toward AI civilization](https://arxiv.org/abs/2411.00114)                                          | arXiv 2024  | [![Star](https://img.shields.io/github/stars/altera-al/project-sid.svg?style=social&label=Star)](https://github.com/altera-al/project-sid)                    |
| [Network Formation and Dynamics Among Multi-LLMs](https://arxiv.org/abs/2402.10659)                                                     | arXiv 2024  | [![Star](https://img.shields.io/github/stars/papachristoumarios/llm-network-formation.svg?style=social&label=Star)](https://github.com/papachristoumarios/llm-network-formation)                    |
| [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://arxiv.org/abs/2307.14984)                     | arXiv 2023  | —                    |
| [Exploring large language models for communication games: An empirical study on werewolf](https://arxiv.org/abs/2309.04658)             | arXiv 2023  | [![Star](https://img.shields.io/github/stars/xuyuzhuang11/Werewolf.svg?style=social&label=Star)](https://github.com/xuyuzhuang11/Werewolf)                     |

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