用图形模型解析故事因果，自动计算悬念与意外。
Shadow-Loom: Causal Reasoning over Graphical World Models of Narratives
- 将故事转为可版本化的图模型，结合因果与反事实推理
- 量化评估悬念、戏剧性反讽等叙事情感状态
- 适合研究叙事结构与计算文学的学者使用
故事吸引读者在于其因果关系、秘密与后果。Shadow-Loom 是一个开源实验框架，将叙事转化为版本化的图形世界模型，并由两个引擎驱动：基于佩尔因果阶梯的因果物理，以及针对祖先多世界网络的反事实微积分；另一引擎则基于施特恩伯格的悬念/惊奇三元组传统，对同一图模型在四种结构化阅读状态——神秘感、戏剧性反讽、悬念与意外——下进行评分。大语言模型仅用于边界任务：信息提取、结果渲染与审计；而识别、干预与反事实推理均在类型化代码中通过图结构完成。该系统作为研究工具发布，非基准化NLP模型，代码、示例与流程均已开源。
原文摘要 · Abstract (English)
Stories hold a reader's attention because they have causes, secrets, and consequences. Shadow-Loom is an experimental open-source framework that turns a narrative into a versioned graphical world model and lets two engines act on it: a causal physics grounded in Pearl's ladder of causation and a recently proposed counterfactual calculus over Ancestral Multi-World Networks; and a narrative physics that scores the same graph against four structural reader-states -- mystery, dramatic irony, suspense, and surprise -- in the tradition of Sternberg's curiosity/suspense/surprise triad, with suspense formalised in the structural-affect line of work on story comprehension and computational suspense. Large language models are used only at the boundary: extraction, rendering, and audit; identification, intervention, and counterfactual reasoning are carried out in typed code over the graph. The system is offered as a research artefact rather than as a benchmarked NLP model; code, fixtures, and pipeline are released open source.
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