Textual Planning with Explicit Latent Transitions
EMBEDPLAN replaces slow next-state generation with a lightweight transition model using frozen embeddings. It learns to predict subsequent states and disambiguate actions within known planning problems. The results show high accuracy for interpolating within familiar problem structures, but generalization fails severely when moving to unseen problems or different domains, indicating embeddings capture surface form, not structural planning roles.