FossilWriter: Learning Hypergraph World Models with Latent Narratives for Creative Story Generation

Abstract

Creative story generation has achieved notable progress with large language models. Current methods construct narratives through hierarchical planning or incremental expansion. These approaches produce structurally complete stories but offer limited support for organic narrative development. Many fiction writers offer a complementary view of the creative process. They often describe their work as gradually uncovering a story embedded in the world they have built. This perspective motivates our approach. We propose that a world model can grow to contain latent narratives as it develops. When characters hold incomplete knowledge and situations remain unresolved, the world structure itself accumulates seeds for story development. Based on this insight we present FossilWriter, a framework that learns hypergraph world models for creative story generation. FossilWriter represents the story world as a shared hypergraph and introduces a narrative layer that marks elements with developmental potential. An excavation module detects and scores latent narratives on this structure. A scene controller selects locations and characters to develop promising narrative threads. The hypergraph grows during writing and serves as the single source of truth to ensure long-range consistency. Experiments on three benchmarks show that FossilWriter achieves an 81.4% win rate on plot coherence and 84.7% on world consistency. Human assessment shows a 32.5% reduction in long-range factual conflicts compared with strong baselines.