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.

翻译：故事因包含起因、秘密与后果而能吸引读者。暗影织机是一个实验性的开源框架，它将叙事转化为版本化的图形世界模型，并让两种引擎作用于该模型：一是基于珀尔因果阶梯理论的因果物理学，以及近期提出的基于祖先多世界网络的反事实演算；二是叙事物理学，该引擎依据斯特恩贝格的"好奇/悬疑/惊喜"三元传统，通过四个结构性读者状态——神秘、戏剧性反讽、悬疑与惊喜——对同一图形进行评分，其中悬疑概念基于故事理解与计算悬疑领域的结构-情感研究路线进行形式化表达。大语言模型仅应用于边界环节：抽取、呈现与审计；而识别、干预及反事实推理均通过类型化代码在图形上执行。该系统作为研究制品而非受基准评测的自然语言模型发布；相关代码、测试用例与处理流程均以开源形式提供。