From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives

Although large language models (LLMs) have demonstrated impressive creative fiction generation, they struggle to maintain narrative consistency and coherent plot lines in long-form stories. In this work, we introduce MAGNET, a multi-agent goal-driven narrative engine for storytelling, which generates stories with persona-grounded character agents that propose actions based on a shared structured state and evolving story goals. We evaluate MAGNET on 20 and 100 page stories using LLM based editor annotations and rubric scoring. At both narrative lengths, MAGNET significantly reduces editor annotations and improves rubric scores compared to single-model prompting, StoryBox, and StoryWriter (p<0.05). Ablation experiments also show that the critic module, structured state, and goal sequencing each contribute significantly to performance (p<0.05). These results suggest that long-form narratives can emerge from explicit structured states, critic-based action refinement, and goal-driven multi-agent generation with loosely specified character personas, providing a foundation for controllable and structurally coherent long-form narrative generation.