A multi-agent framework generates and refines executable physics simulation code from prompts to create world models that enforce physical constraints, claiming superior accuracy and fidelity over video-based alternatives.
Dream to control: Learning behaviors by latent imagination
3 Pith papers cite this work. Polarity classification is still indexing.
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The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.
Action-state consistency in World Action Models distinguishes successful from failed imagined futures and supports value-free selection of better rollouts via consensus among predictions.
citing papers explorer
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Coding Agent Is Good As World Simulator
A multi-agent framework generates and refines executable physics simulation code from prompts to create world models that enforce physical constraints, claiming superior accuracy and fidelity over video-based alternatives.
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Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.
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Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models
Action-state consistency in World Action Models distinguishes successful from failed imagined futures and supports value-free selection of better rollouts via consensus among predictions.