arXiv:2604. 16592v2 Announce Type: replace-cross Abstract: This report of world models distinguishes prior works by the cognitive functions they innovate.
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The paper introduces WROP, a dataset of 150 hand‑designed cognitive tasks aimed at testing object permanence in video generation models. Using Blender, the authors generate over 10,000 samples per task, compiling a 1.5 M‑sample training corpus and a 300‑question exam. They evaluate 14 video models, showing that their 16B world model, PWM‑WROP, ranks first among continuation models and third overall in a blind Elo study.
arXiv:2507. 05169v5 Announce Type: replace-cross Abstract: World Model, the algorithmic simulator of the real-world environment which biological agents experience and act upon, has been an emerging topic in recent years due to the rising need to develop virtual agents with artificial (general) intelligence.
arXiv:2507. 05169v4 Announce Type: replace-cross Abstract: World Model, the algorithmic simulator of the real-world environment which biological agents experience and act upon, has been an emerging topic in recent years due to the rising need to develop virtual agents with artificial (general) intelligence.
arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.
arXiv:2609.05834v1 Announce Type: new Abstract: World models promise a general route to embodied intelligence: learn predictive dynamics once, then reason, plan, and act with them. Increasingly, the...
arXiv:2607. 06401v1 Announce Type: new Abstract: World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI.