A robot world model that serves as a simulator must do more than generate plausible videos: it must faithfully follow commanded actions, maintain consistency across camera views, and sustain coherent
PrimeSim is an action-conditioned multiview world model that uses progressive training, unified action representations, view-aware attention, and hierarchical memory to enable consistent, long-horizon robot simulation and policy optimization.
A robot world model that serves as a simulator must do more than generate plausible videos: it must faithfully follow commanded actions, maintain consistency across camera views, and sustain coherent long-horizon rollouts. We present PrimeSim, an action-conditioned, multiview world model simulator that addresses these challenges through progressive midtraining and posttraining. Starting from a pretrained video generator, we midtrain on 30,110 hours of robot demonstrations spanning 22 platforms. To improve action following, we introduce a unified cross-embodiment action representation and frame-aligned dual-path conditioning to strengthen action-frame correspondence. To maintain multiview…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
PrimeSim: A Generalist World Model Simulator from Large-Scale Cross-Embodiment Demonstrations was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 11 checks on 2 different days. Its best position was #19 of 30, reached on Nov 4, 05:28 UTC. It dropped off the list after Nov 5, 01:28 UTC.
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It was first seen on Nov 4, 2026 and has been on the list on 2 days (11 checks) since.