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Towards Enhanced Immersion and Agency for LLM-based Interactive Drama

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arxiv 2502.17878 v2 pith:FWK3RAXW submitted 2025-02-25 cs.CL

Hongqiu Wu , Weiqi Wu , Tianyang Xu , Jiameng Zhang , Hai Zhao This is my paper
classification cs.CL
keywords interactiveplayerstoryagencydramaimmersionagentsaspects
verification ladder T0 review T1 audit T2 compute T3 formal
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LLM-based Interactive Drama is a novel AI-based dialogue scenario, where the user (i.e. the player) plays the role of a character in the story, has conversations with characters played by LLM agents, and experiences an unfolding story. This paper begins with understanding interactive drama from two aspects: Immersion, the player's feeling of being present in the story, and Agency, the player's ability to influence the story world. Both are crucial to creating an enjoyable interactive experience, while they have been underexplored in previous work. To enhance these two aspects, we first propose Playwriting-guided Generation, a novel method that helps LLMs craft dramatic stories with substantially improved structures and narrative quality. Additionally, we introduce Plot-based Reflection for LLM agents to refine their reactions to align with the player's intentions. Our evaluation relies on human judgment to assess the gains of our methods in terms of immersion and agency.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Too Human to Model:The Uncanny Valley of LLMs in Social Simulation -- When Generative Language Agents Misalign with Modelling Principles

    cs.CY 2025-07 conditional novelty 7.0 of 10

    A position paper contends that LLM agents, despite their human-like talk, are often too rich in detail to serve as scientific models, and proposes conditions where they still excel.

  2. CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A role-playing LLM that reasons about the scene and its own state before responding, trained with two semantic rewards, beats stronger baselines on role-play benchmarks.

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