MUSE: A Theory-Harnessed Story Engine for Vibe Narrativizing

LLMs can generate fluent prose. Turning this capability into high-quality stories requires coordinating decisions about plot, character, and language across planning, drafting, and revision. Guiding these decisions presents two bottlenecks: the quality of story guidance and its sustained use. We formulate Vibe Narrativizing as the task of turning natural-language writing requirements into a finished story and present MUSE, a Theory-Harnessed Story Engine. MUSE derives reusable guidance from Robert McKee's story theory through rule atomization, semantic consolidation, and mechanism abstraction. A single source of truth and layered disclosure organize this guidance, while examples clarify principles that depend on context and aesthetic judgment. An agent harness preserves creative decisions in intermediate deliverables across design, character performance, scene composition, and revision. Context engineering supplies each role with relevant guidance and decisions; a masterwork corpus provides inspiration and prose references. A worked example traces a requested object from its thematic role to climactic actions. Across four base models, MUSE improves WritingBench by 1.6--4.8 points over zero-shot generation and raises LongStoryEval by more than ten points on three. ConStory-Bench consistency error density remains in the low single digits for all four models, below every reproduced story-system baseline on three. Ablations locate the largest quality contribution in structural design, voice-specific effects in the character path, and further gains in revision.