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HyperWorld: Hypergraph-Structured State Serialization Improves Learned Textual World Models

This story is from 2026-09-02. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.00002v1 Announce Type: new Abstract: 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 un…

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  1. 2026-09-02 04:00 · arXiv cs.AI
    HyperWorld: Hypergraph-Structured State Serialization Improves Learned Textual World Models

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