Foreshadowing and payoff are ubiquitous narrative devices through which authors introduce commitments early in a story and resolve them through concrete, observable outcomes. However, despite advances in story generation, large language models (LLMs) frequently fail to bridge these long-range narrative dependencies, often leaving "Chekhov's guns" unfired even when the necessary context is present. Existing evaluations largely overlook this structural failure, focusing on surface-level coherence rather than the logical fulfillment of narrative setups. In this paper, we introduce Codified Foreshadowing-Payoff Generation (CFPG), a novel framework that reframes narrative quality through the lens of payoff realization. Recognizing that LLMs struggle to intuitively grasp the "triggering mechanism" of a foreshadowed event, CFPG transforms narrative continuity into a set of executable causal predicates. By mining and encoding Foreshadow-Trigger-Payoff triples from the BookSum corpus, we provide structured supervision that ensures foreshadowed commitments are not only mentioned but also temporally and logically fulfilled. Experiments demonstrate that CFPG significantly outperforms standard prompting baselines in payoff accuracy and narrative alignment. Our findings suggest that explicitly codifying narrative mechanics is essential for moving LLMs from surface-level fluency to genuine narrative competence.

翻译：伏笔与呼应是普遍存在的叙事手法，作者通过它们在故事早期引入承诺，并通过具体可观察的结果予以解决。然而，尽管故事生成技术有所进展，大型语言模型（LLMs）在连接这些长程叙事依赖关系时仍经常失败，即使必要的上下文存在，也常常留下未响应的“契诃夫之枪”。现有的评估在很大程度上忽视了这种结构性失败，侧重于表面连贯性而非叙事铺垫的逻辑实现。本文提出编码化伏笔-呼应生成（CFPG），这是一个通过呼应实现视角重构叙事质量的新颖框架。认识到LLMs难以直观把握伏笔事件的“触发机制”，CFPG将叙事连续性转化为一组可执行的因果谓词。通过从BookSum语料库中挖掘并编码“伏笔-触发-呼应”三元组，我们提供了结构化监督，确保伏笔承诺不仅被提及，而且在时间和逻辑上得到实现。实验表明，CFPG在呼应准确性和叙事一致性方面显著优于标准提示基线。我们的研究结果表明，显式编码叙事机制对于推动LLMs从表面流畅性迈向真正的叙事能力至关重要。