Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction
A new AI system called Narrative World Model (NWM) helps fiction writers track complex story states using narratology-grounded temporal graphs, outperforming existing memory frameworks on multi-hop narrative understanding.
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[Submitted on 6 Jul 2026]
Title:Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction
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Abstract: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 a single held-constant Opus 4.8 reader over only that system's chapter-safe evidence, on a reproducible public corpus and a validated multi-hop benchmark, and we compare against the strongest existing temporal-knowledge-graph agent-memory framework, Graphiti/Zep (Rasmussen et al., 2025). NWM substantially and significantly outperforms this baseline on multi-hop narratological QA across both corpora, and far exceeds GraphRAG and flat retrieval. The advantage is representational rather than an artifact of extraction: it survives rebuilding the baseline with NWM's own extractor, and traces to its narratology-grounded structure and query-conditioned retrieval, not to graph size or extractor quality.
Comments: 23 pages, 4 figures; 9-page main text plus appendix. Preprint
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2607.05577 [cs.AI]
(or arXiv:2607.05577v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.05577
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mohammad Saifullah [view email] [v1] Mon, 6 Jul 2026 19:23:52 UTC (101 KB)
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