Distinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation

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. 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. 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. 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.

Accepted as a full paper for presentation at the 2026 Workshop on Computational Models of Narrative (CMN 2026). This preprint corresponds to the workshop version

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multimedia (cs.MM)

arXiv:2608.21364 [cs.CL]

(or arXiv:2608.21364v1 [cs.CL] for this version)