Challenge: Existing methods for long-form complex narrative generation struggle to maintain global narrative coherence and logical consistency.
Approach: They propose a framework that performs narrative planning on structural graph representations instead of direct sequential text representations.
Outcome: The proposed model outperforms representative baselines across diverse scenarios.

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Challenge: Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience.
Approach: They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story.
Outcome: The proposed approach outperforms existing methods in 84.33% of the trials.
StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation (2026.acl-long)

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Challenge: Existing approaches augment reasoning steps or inject specific structure into how models think, but leave scattered problem conditions unchanged.
Approach: They propose a narrative reformulation framework that transforms code generation questions into coherent natural language narratives.
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Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives (2024.findings-emnlp)

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Challenge: Large language models (LLMs) have demonstrated impressive performance in many reasoning tasks, but temporal reasoning remains challenging due to its intrinsic complexity.
Approach: They propose a new prompting technique tailored for temporal reasoning, Narrative-of-Thought (NoT), that first converts the events set to a Python class, then prompts a small model to generate a temporal narrative.
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NGEP: A Graph-based Event Planning Framework for Story Generation (2022.aacl-short)

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Challenge: Current approaches to story generation are based on end-to-end neural generation models, such as BART, to generate event sequences.
Approach: They propose a novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor.
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Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books (2026.findings-acl)

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Challenge: Character description generation is an important capability for narrative-focused applications . however, generating accurate character descriptions from long-form narratives is challenging . enabling built-in reasoning mode of current LLMs often degrades performance .
Approach: They propose a framework that decouples reasoning from generation by generating a structured QA reasoning trace and a generation model that conditions on this trace to produce the final character description.
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Are Large Language Models Capable of Generating Human-Level Narratives? (2024.emnlp-main)

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Challenge: a recent HCI study has pointed to gaps in machine storytelling ability at the global level . authors show that LLMs have less suspense and less tension than human stories .
Approach: They propose a computational framework to analyze narratives through three discourse-level aspects.
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Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLMs (2025.emnlp-main)

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Challenge: Large Reasoning Models (LRMs) often display unstable behaviors, e.g., hallucinating unsupported premises, overthinking simple tasks, and displaying higher sensitivity to prompt variations.
Approach: They propose a graph-based analytical framework that clusters long, verbose CoT outputs into semantically coherent reasoning steps, then constructs directed reasoning graphs to capture contextual and logical dependencies among these steps.
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A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)

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Challenge: Existing approaches to modeling media narratives miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability.
Approach: They propose a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering.
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Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding (2023.findings-emnlp)

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Challenge: Large language models (LLMs) excel in generating coherent texts, but their ability to comprehend the author’s thoughts remains uncertain.
Approach: They conduct a comprehensive survey of narrative understanding tasks, examining their key features, definitions, taxonomy, associated datasets, evaluation metrics, and limitations.
Outcome: The proposed framework could be extended to address novel narrative understanding tasks.
Unifying Inference-Time Planning Language Generation (2026.findings-acl)

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Challenge: Large language models (LLMs) are used to generate a formal representation of a plan in a planning language.
Approach: They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning.
Outcome: The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages.

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