dorsal/arxiv
View SchemaLitVISTA: A Benchmark for Narrative Orchestration in Literary Text
| Authors | Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, Jiarui Zhang, Yue Hu, Yunpeng Li |
|---|---|
| Categories | |
| ArXiv ID | 2601.06445vv1 |
| URL | https://arxiv.org/abs/2601.06445 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can generate long stories but overly focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. This creates a structural misalignment between model- and human-generated narratives. We propose VISTA Space, a high-dimensional representational framework for narrative orchestration that unifies human and model narrative perspectives. We further introduce LitVISTA, a structurally annotated benchmark grounded in literary texts, enabling systematic evaluation of models' narrative orchestration capabilities. We conduct oracle evaluations on a diverse selection of frontier LLMs, including GPT, Claude, Grok, and Gemini. Results reveal systematic deficiencies: existing models fail to construct a unified global narrative view, struggling to jointly capture narrative function and structure. Furthermore, even advanced thinking modes yield only limited gains for such literary narrative understanding.
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"abstract": "Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can generate long stories but overly focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. This creates a structural misalignment between model- and human-generated narratives. We propose VISTA Space, a high-dimensional representational framework for narrative orchestration that unifies human and model narrative perspectives. We further introduce LitVISTA, a structurally annotated benchmark grounded in literary texts, enabling systematic evaluation of models\u0027 narrative orchestration capabilities. We conduct oracle evaluations on a diverse selection of frontier LLMs, including GPT, Claude, Grok, and Gemini. Results reveal systematic deficiencies: existing models fail to construct a unified global narrative view, struggling to jointly capture narrative function and structure. Furthermore, even advanced thinking modes yield only limited gains for such literary narrative understanding.",
"arxiv_id": "2601.06445",
"authors": [
"Mingzhe Lu",
"Yiwen Wang",
"Yanbing Liu",
"Qi You",
"Chong Liu",
"Ruize Qin",
"Haoyu Dong",
"Wenyu Zhang",
"Jiarui Zhang",
"Yue Hu",
"Yunpeng Li"
],
"categories": [
"cs.CL",
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "LitVISTA: A Benchmark for Narrative Orchestration in Literary Text",
"url": "https://arxiv.org/abs/2601.06445",
"version": "v1"
},
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"variant": "snapshot-2026-01-17",
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