用LLM和蒙特卡洛树搜索实现故事分支探索，支持多路径剧情创作。
Narrative Studio: Visual narrative exploration using LLMs and Monte Carlo Tree Search
- 基于LLM与树状界面，支持用户在任意节点自由分支叙事。
- 通过用户定义标准自动扩展高潜力剧情路径，提升多样性。
- 结合实体图谱增强角色与环境一致性，保障故事连贯性。
互动叙事需要规划和探索多种‘如果……会怎样’的情景。现代大语言模型（LLM）可用于创意构思与探索，但当前基于聊天的界面限制了用户只能沿单一线性流程进行。为解决这一局限，我们提出Narrative Studio——一个基于浏览器的叙事探索环境，采用树状结构界面，允许用户在故事任意节点进行分支探索。每个分支通过迭代式LLM推理延伸，由系统和用户定义的提示引导。此外，我们引入蒙特卡洛树搜索（MCTS），依据用户指定标准自动拓展有前景的叙事路径，实现更丰富、更稳健的故事生成。同时，用户可通过将生成文本锚定于代表故事角色与环境的实体图谱，增强叙事连贯性。
原文摘要 · Abstract (English)
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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