待翻譯：Distinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.21364v1 Announce Type: new 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.
AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [Submitted on 10 Jun 2026] Title:Distinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation View a PDF of the paper titled Distinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation, by Yi-Chun Chen View PDF HTML (experimental) 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. Comments: 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 Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multimedia (cs.MM) Cite as: arXiv:2608.21364 [cs.CL] (or arXiv:2608.21364v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.21364 arXiv-issued DOI via DataCite Submission history From: Yi-Chun Chen [view email] [v1] Wed, 10 Jun 2026 19:56:08 UTC (1,829 KB) Full-text links: Access Paper: View a PDF of the paper titled Distinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation, by Yi-Chun Chen View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-08 Change to browse by: cs cs.AI cs.MM References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)