World Models Survey (arXiv)

2026

1. arXiv

   Human Cognition in Machines: A Unified Perspective of World Models

   Timothy Rupprecht^*, Pu Zhao^*, Amir Taherin^*, and 20 more authors

   2026

   Abs arXiv Bib

   This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To evaluate these claims requires a proper grounding in first principles from human and machine cognition theory. In moving towards human-like world models we present a conceptual unified framework for world models that fully incorporates all the cognitive functions (i.e., memory, perception, language, reasoning, imagining, motivation, and metacognition) and identify gaps in existing research as a guide for future states of the art. In particular, we find that motivation (especially intrinsic motivation) and metacognition remain drastically under-researched, and we propose concrete directions to address these gaps informed by active inference and global workspace theory. We also introduce epistemic world models, a new category encompassing agent frameworks for scientific discovery that operate over structured knowledge. Our taxonomy, applied to video, embodied, and epistemic world models, suggests research directions where prior taxonomies have not.

   @misc{rupprecht2026human,
     title = {Human Cognition in Machines: A Unified Perspective of World Models},
     author = {Rupprecht, Timothy and Zhao, Pu and Taherin, Amir and Akbari, Arash and Akbari, Arman and He, Yumei and Imtiaz, Tooba and Duffy, Sean and Lin, Juyi and Chen, Yixiao and Chowdhury, Rahul and Nan, Enfu and Shen, Yixin and Cao, Yifan and Zeng, Haochen and Chen, Weiwei and Yuan, Geng and Dy, Jennifer and Ostadabbas, Sarah and Zhang, Xuan and Kaeli, David and Yeh, Edmund and Wang, Yanzhi},
     year = {2026},
   }