# Story Generation

Large language models write stories that often lack the rising and falling reader experience that makes narrative engaging. A new framework first learns how reader responses such as suspense or humor evolve in high-quality human-written stories, then uses a small model to predict the response the next segment should elicit and steers an off-the-shelf writer model with that prediction. Across suspense and humor, with GPT-5.5 and Qwen3.5-27B as writers, generated stories improve trajectory-based fidelity to human-written stories by 12%–35% while largely preserving story coherence.

Large language models write stories that often lack the rising and falling reader experience that makes narrative engaging. A new framework first learns how reader responses such as suspense or humor evolve in high-quality human-written stories, then uses a small model to predict the response the next segment should elicit and steers an off-the-shelf writer model with that prediction. Across suspense and humor, with GPT-5.5 and Qwen3.5-27B as writers, generated stories improve trajectory-based fidelity to human-written stories by 12%–35% while largely preserving story coherence.