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HyperWorld: Hypergraph-Structured State Serialization Improves Learned Textual World Models
World models enable language-model agents to predict environment dynamics and plan before acting. In text environments, the model must learn symbolic action effects from serialized state descriptions, but the role of serialization structure remains underexplored. We present HyperWorld, a controlled
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