Understanding World or Predicting Future? A Comprehensive Survey of World Models
On the map: World models → World models for language agents
Abstract (arXiv)
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models. Generally, world models are regarded as tools for either understanding the present state of the world or predicting its future dynamics. This review presents a systematic categorization of world models, emphasizing two primary functions: (1) constructing internal representations to understand the mechanisms of the world, and (2) predicting future states to simulate and guide decision-making. Initially, we examine the current progress in these two categories. We then explore the application of world models in key domains, including generative games, autonomous driving, robotics, and social simulacra, with a focus on how each domain utilizes these aspects. Finally, we outline key challenges and provide insights into potential future research directions. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/World-Model.
Key facts. Cited by 161 papers in the map (data through 7 Oct 2026). Placed in World models for language agents, one of 11 directions in World models. Builds on 211 of its 340 references that are in the map. First posted on arXiv 21 Nov 2024.
Builds on
211 of 340 references.
Closest ancestors · 36
Same direction, newest first
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- 2024A Survey on the Memory Mechanism of Large Language Model based Agents21 Apr
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- 2023ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification17 Oct
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- 2023Emergent Representations of Program Semantics in Language Models Trained on Programs18 May
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- 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learning8 Oct
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Other references · 175
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50 most cited of 175 All 175 (JSON)
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Citing papers per year: 2024: 1; 2025: 65; 2026: 95.
161 papers.
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- 2025A Comprehensive Survey on World Models for Embodied AIcited by 65
- 20253D and 4D World Modeling: A Surveycited by 64
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- 2025A Survey: Learning Embodied Intelligence from Physical Simulators and World Modelscited by 50
- 2025RoboScape: Physics-informed Embodied World Modelcited by 38
- 2025ViMo: A Generative Visual GUI World Model for App Agentscited by 33
- 2025The Role of World Models in Shaping Autonomous Driving: A Comprehensive Surveycited by 29
- 2025Embodied AI: From LLMs to World Modelscited by 23
- 2026WorldCompass: Reinforcement Learning for Long-Horizon World Modelscited by 22
- 2026MobileDreamer: Generative Sketch World Model for GUI Agentcited by 21
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- 2026World-as-Graph: Relational World Modeling Through Latent Space Graphs30 Sep 2026
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- 2026TrojanWorld: Backdooring World-Model Agents via Imagination Steering7 Sep 2026
- 2026Predicting Consequences and Reinforcing Navigation Policies with Latent World Models23 Aug 2026
- 2026CIVA: Critic-Induced Value-Subspace Attacks on Visual World-Model Agents21 Aug 2026
- 2026ADAPT: Physics-Aware Diffusion-based World Models for Adaptive Predictive Transferable HVAC Control20 Aug 2026
- 2026Physiological World Models for Human State Transitions15 Aug 2026
- 2026A Unifying Perspective on Causal World Models: From Observations to Representations to Structure13 Aug 2026
- 2026From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models6 Aug 2026
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- 2026Can Machines Really See Objects in Images? A Study Based on Syntactic Distance and Visual Self-Referential Instances28 Jun 2026
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