Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction
Long-form fiction writers need memory that answers multi-hop questions about evolving story state: who knows a secret and when they learned it, whether an event preceded the narration that revealed it, whether a setup paid off, and how a relationship shifted. General-purpose retrieval and agent-memory systems represent entities and facts but not the narratological structure these questions turn on, so they surface the wrong evidence or none at all. We introduce the Narrative World Model (NWM), a writer-memory system that pairs a narratology-grounded typed temporal-state graph with query-conditioned hybrid retrieval. To measure memory rather than the answerer, we read every system through ...
| Authors | Mohammad Saifullah, Thomas Kornmaier, Taaha Kazi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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