MUSE: A Theory-Harnessed Story Engine for Vibe Narrativizing
LLMs have been able to generate fluent prose, but high-quality stories also require coordinated decisions about plot, character, and language across planning, drafting, and revision. We formulate Vibe Narrativizing as turning natural-language writing requirements into a finished story. MUSE, a Theory-Harnessed Story Engine, addresses two bottlenecks: rule quality and sustained rule realization. Story theory supplies the rules, and a practical agent harness puts them to work. Knowledge engineering organizes Robert McKee's theory through rule atomization, semantic consolidation, mechanism abstraction, a single source of truth, and layered disclosure; typical examples clarify judgments that ...
| Authors | Jianxiang Ma, Xiaocui Yang, Daling Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 September 2026 |
| Last updated at source | 13 October 2026 |
Connected in the knowledge graph
- RoadtoASI/MUSE-skillsDataset · cited by
Models and datasets referencing this paper
- RoadtoASI/MUSE-skillsDataset
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