Distinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation

区分增量叙事理解中的“修订”与“延迟阐述”

Abstract: Both human and AI systems that process narrative or long-form content operate incrementally: input is received over time, and internal representations must be updated accordingly. Incremental interpretation, therefore, depends not only on what is represented but also on how the representational state evolves under new evidence.

摘要： 无论是人类还是处理叙事或长篇内容的人工智能系统，其运作方式都是增量的：输入随时间推移而接收，内部表征也必须随之更新。因此，增量理解不仅取决于表征的内容，还取决于表征状态如何根据新证据进行演变。

We distinguish two structurally different update operators that arise in narrative interpretation: revision-driven update and delayed elaboration. Revision-driven updates retract or replace previously committed structure in response to a contradiction and are therefore non-monotonic. Delayed elaboration, by contrast, refines initially underspecified elements through constraint addition without retracting prior commitments, yielding monotonic extension of the interpretive state.

我们区分了叙事理解中出现的两种结构上截然不同的更新算子：修订驱动的更新（revision-driven update）和延迟阐述（delayed elaboration）。修订驱动的更新是为了响应矛盾而撤回或替换先前已确定的结构，因此是非单调的。相比之下，延迟阐述通过增加约束来细化最初未明确的元素，而无需撤回先前的承诺，从而实现解释状态的单调扩展。

Although both operators may alter how earlier material is understood, they impose fundamentally different structural requirements on state transitions. Using visual narratives as a diagnostic domain, we demonstrate how a structured narrative representation can explicitly separate committed from underspecified content and support both update operators during incremental construction.

尽管这两种算子都可能改变对早期内容的理解，但它们对状态转换提出了根本不同的结构要求。我们以视觉叙事作为诊断领域，展示了结构化叙事表征如何明确区分已确定内容与未明确内容，并在增量构建过程中支持这两种更新算子。

Through a worked example, we show how delayed elaboration enables monotonic refinement of interpretive state, while revision requires non-monotonic correction. We discuss the broader relevance of this structural distinction for incremental reasoning and hybrid symbolic-neural systems.

通过一个具体示例，我们展示了延迟阐述如何实现解释状态的单调细化，而修订则需要非单调的修正。我们讨论了这种结构性区分对于增量推理和混合符号-神经系统的更广泛意义。