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 (...
| Authors | Chloe Ho, Aayush Aluru, Muhammad Hammouri et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 July 2026 |
| Last updated at source | 3 October 2026 |
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