HyperWorld Shows Hypergraph State Serialization Improves Textual World Models

Summary

HyperWorld studies how state serialization affects learned textual world models for language-model agents. Entity-centered hyperedge units provide the clearest gains for 0.5B–1.5B models and under distribution shift, while achieving the highest downstream greedy-planning success rate. Larger models reduce the difference, and pairwise triples can remain competitive on in-distribution exact match.