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Narrative Studio: Visual narrative exploration using LLMs and Monte Carlo Tree Search

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arxiv 2504.02426 v1 pith:R6N2TPHH submitted 2025-04-03 cs.AI

classification cs.AI
keywords narrativeexplorationstorycarloenvironmentllmsmontesearch
verification ladder T0 review T1 audit T2 compute T3 formal
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Interactive storytelling benefits from planning and exploring multiple 'what if' scenarios. Modern LLMs are useful tools for ideation and exploration, but current chat-based user interfaces restrict users to a single linear flow. To address this limitation, we propose Narrative Studio -- a novel in-browser narrative exploration environment featuring a tree-like interface that allows branching exploration from user-defined points in a story. Each branch is extended via iterative LLM inference guided by system and user-defined prompts. Additionally, we employ Monte Carlo Tree Search (MCTS) to automatically expand promising narrative paths based on user-specified criteria, enabling more diverse and robust story development. We also allow users to enhance narrative coherence by grounding the generated text in an entity graph that represents the actors and environment of the story.

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Cited by 3 Pith papers

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

  1. Rushes: A Human Preference Dataset for Pluralistic Alignment

    cs.CL 2026-07 conditional novelty 6.0 of 10

    In six AI-written interactive games, players' 44,226 logged choices are predicted better by a simple SVD recommender (37.7%) and a popularity rule (36.4%) than by GPT-5 with the player's history (34.2%).

  2. InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluation

    cs.HC 2025-12 conditional novelty 6.0 of 10

    An interactive tree visualization of LLM sampling lets evaluators cover the same harmful-response space as random sampling with up to 5x fewer samples.

  3. Avoidance Decoding for Diverse Multi-Branch Story Generation

    cs.CL 2025-09 conditional novelty 5.0 of 10

    Avoidance Decoding penalizes token choices that resemble previously generated story branches, using a hybrid concept-level and narrative-level similarity penalty, and reports large diversity gains across several LLMs.

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