Narrative Studio: Visual narrative exploration using LLMs and Monte Carlo Tree Search
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912307230015488 |
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| author | Ghaffari, Parsa Hokamp, Chris |
| author_facet | Ghaffari, Parsa Hokamp, Chris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02426 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Narrative Studio: Visual narrative exploration using LLMs and Monte Carlo Tree Search Ghaffari, Parsa Hokamp, Chris Artificial Intelligence 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. |
| title | Narrative Studio: Visual narrative exploration using LLMs and Monte Carlo Tree Search |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.02426 |