Introduces looped transformer architectures for world models that iteratively refine latent states to achieve up to 100x parameter efficiency via adaptive computation depth.
World models for autonomous driving: An initial survey.IEEE Transactions on Intelligent Vehicles
3 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Infrastructure-centric world models use roadside sensors' temporal depth to complement vehicle spatial breadth for better traffic simulation and prediction.
The paper delivers a multi-axis taxonomy for world models that maps architectures, training families, reasoning strategies, and domains from early cognitive foundations through systems such as Dreamer, MuZero, and Sora while noting evaluation gaps.
citing papers explorer
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Looped World Models
Introduces looped transformer architectures for world models that iteratively refine latent states to achieve up to 100x parameter efficiency via adaptive computation depth.
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Infrastructure-Centric World Models: Bridging Temporal Depth and Spatial Breadth for Roadside Perception
Infrastructure-centric world models use roadside sensors' temporal depth to complement vehicle spatial breadth for better traffic simulation and prediction.
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World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications
The paper delivers a multi-axis taxonomy for world models that maps architectures, training families, reasoning strategies, and domains from early cognitive foundations through systems such as Dreamer, MuZero, and Sora while noting evaluation gaps.