HyperWorld: hypergraph-structured state serialization improves learned textual world models
Read the original at arxiv.org→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...
Original headline: "HyperWorld: Hypergraph-Structured State Serialization Improves Learned Textual World Models"
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- Sep 2, 04:00 UTC arXiv cs.AI lead source HyperWorld: Hypergraph-Structured State Serialization Improves Learned Textual World Models