Narrative Studio: Visual narrative exploration using LLM s and M onte C arlo Tree Search (2025)

Abstract: 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 a graph that represents the actors and environment of the story.

## introduction large language models (llms) have significantly advanced the field of automated narrative generation, demonstrating impressive capabilities in producing coherent and contextually rich stories @xcite . however, most user interfaces designed for interacting with llms remain constrained to linear progression, limiting creative exploration and the ability to engage with alternative narrative possibilities. in domains such as interactive storytelling, game design, and creative writing, users often wish to explore multiple "what-if" scenarios, comparing different narrative trajectories in parallel @xcite , and necessarily generating exponential possible paths as story length grows. existing llm-powered systems, exposed primarily as chat-based interfaces, do not provide a structured way to navigate these non-linear narrative spaces. existing work has explored branching narrative systems that enable users to make choices leading to different outcomes. prior work in game narratives and mixed-initiative storytelling has demonstrated the potential of branching structures to enhance engagement by offering multiple paths for exploration @xcite . however, many such systems rely on pre-scripted paths or manually defined rules, limiting flexibility and scalability. additionally, ensuring narrative coherence across branches remains a persistent challenge, as diverging storylines may lead to inconsistencies in character motivations, world states, or causal/temporal relationships. in this work, we propose narrative studio, a novel in-browser narrative exploration environment that allows users to simultaneously develop multiple story branches while preserving coherence through iterative llm inference. the core novelty of our approach is the unification of a tree-based interface, iterative cause-and-effect expansions, and search-based expansions under mcts, enabling a structured yet highly flexible branching mechanism for interactive story generation. by combining these elements, our system provides authors with a versatile environment to explore parallel storylines, identify interesting outcomes, and resolve or prevent consistency issues. tree-based user interface our approach leverages a tree-based user interface, where branching points are user-defined or llm-generated, enabling structured yet flexible exploration. to maintain narrative consistency, we ground an llm in prior events with cause-and-effect conditioning, ensuring coherence across diverging paths. furthermore, we integrate monte carlo tree search (mcts) to autonomously expand promising branches based on default or user-specified criteria, thereby reducing reliance on pre-scripted structures while enhancing narrative discovery. knowledge graph grounding story entities and environments are represented in a graph, which serves as a grounding mechanism for the generated text. graph-based methods have been explored in narrative analysis for tracking relationships between characters, events, and objects, but their integration into interactive storytelling tools remains underdeveloped. by incorporating a structured representation of key entities, our approach ensures logical consistency and continuity across multiple branching narratives. our contributions foot_0 are as follows: the remainder of this paper is structured as follows: section 2 discusses related work in story generation, interactive storytelling, and evaluation of narrative generation. section 3 presents the methodology behind narrative studio, including its user interface, mcts integration, and graphbased grounding. in section 4, we outline experimental setups and evaluation metrics, followed by a discussion of our findings in section 5. section 6 concludes with suggestions for future research directions. ## experimental setup we focus our evaluations on measuring the effectiveness of mcts-based narrative generation, and in order to do so, we apply it to a set of 20 story "stubs"-short initial contexts-randomly selected from the publicly available children stories text corpus foot_2 . this dataset, compiled from cleaned public-domain project gutenberg children's books foot_3 , provides a diverse range of introductory story fragments. we run four different mcts configurations alongside three baseline strategies, resulting in seven total strategies (outlined in we apply the llm-based judge described in section 2.4 to each completed story, obtaining numerical ratings (1-10) for seven categories and a short explanatory comment. in section 5, we report aggregated scores for each strategy across the 20 stubs. note on model variants. we employ a slightly less capable llm from the "gpt-4o" family to generate forward and backward expansions, while the "judge" agent uses a more advanced "o1" model variant (both from openai). although the judge thus has comparatively stronger reasoning abilities, relying on any single llm to both generate and evaluate narratives still has limitations (e.g., bias, potential overfitting to certain writing styles). in future work, we plan more extensive human evaluations to triangulate these results. ## results and discussion increasing scoringdepth from 1 to 3 boosts or matches performance, suggesting a longer lookback in the scoring prompt helps detect inconsistencies and refine causal/temporal logic. among the high-capacity configurations (@xmath0, a 100-iteration search with @xmath1, indicating that deeper searches consistently improve coherence, consistency, and flaw detection. nevertheless, a smaller configuration (@xmath2, @xmath3, @xmath4, which suggests moderate-scale mcts often suffices while reducing computational cost. these results confirm that search-based expansions, guided by a well-chosen scoring objective, can produce more coherent and consistent continuations than simple linear generation. however, our automated measurements rely on a single llmbased evaluator, and a more thorough user study might uncover additional nuances in perceived story quality and engagement. we also examined lexical diversity and found no meaningful difference in distinct-n scores (for @xmath5; details appear in appendix e. this suggests that lexical diversity owes more to the local eventgeneration step than the higher-level strategy. comparison to what-if @xcite . while both approaches generate branching narratives via iterative llm calls, what-if leverages meta-prompts and a three-act structure to rewrite a single, linear human-written plot, requiring user input for interactive expansion. in contrast, our framework offers three modes: fully interactive (where the user directs the story), fully automated (where mcts explores and expands branches on its own), or a hybrid of both. by employing a search-based strategy plus a configurable scoring function, we systematically identify and refine the most promising branches rather than relying solely on fixed decision points extracted from an existing storyline. ## conclusion and future work in this paper, we introduced a tree-based narrative exploration environment that applies monte carlo tree search to improve story expansion beyond linear, sequential generation. our results show that mcts-enhanced branching yields more coherent, causally consistent continuations and better identification of major narrative flaws, with deeper lookback in scoring providing an additional boost in quality. although the automated judgments offer compelling evidence of mcts's effectiveness, several avenues remain to be explored. first, we plan a formal human evaluation of the generated stories to verify whether the observed gains align with readers' subjective impressions of coherence and engagement. second, although basic forms of mixedinitiative control already appear in our framework, an in-depth evaluation of a hybrid mcts-human author collaboration approach would clarify how best to integrate user input with algorithmic search, and the performance of such a system relative to the automated strategies explored thus far. third, we will undertake more focused hci evaluations of the interface itself, studying how effectively authors can branch, compare, and refine narratives within our tree-based environment. finally, we aim to learn the mcts objective over multiple iterations of authoring sessions or from large corpora, so that the system's search heuristics and scoring prompts can adapt automatically to different genres, tones, or user preferences, including specialized styles such as horror, comedy, or romance. we believe these directions will further solidify mcts-based branching as a powerful tool for interactive storytelling and creative writing.