The paper presents a new LLM‑based narrative platform that allows interchangeable Experience Managers (EMs) to operate over a shared symbolic world model, enabling explicit preconditions and effects for every operation. By representing each session as a plan in a common state‑transition system, the authors evaluate three different managers—open generation, Monte Carlo repair, and autonomous characters—on four properties: player agency, EM controllability, author intent, and robustness, using an interactive Little Red Riding Hood scenario. The results demonstrate that no single manager excels in all properties, providing authors and designers with concrete criteria to choose a manager that best fits their priorities.
By Jules Clerc (Heudiasyc), Domitile Lourdeaux (Heudiasyc), Mohamed Sallak (Heudiasyc), Marc Ravaine
arXiv:2608. 04037v1 Announce Type: cross Abstract: Designing narrative-grounded interactive experiences remains labor-intensive because interactive content must align with the underlying world implied by the narrative.
By Yi-Chun Chen
The paper introduces Forking Garden, a branching game generation system that threads narrative archetype as a persistent semantic signal throughout the generation pipeline. Narrative progression is modeled with soft Rise/Fall states, guiding the creation of plot nodes, structural constraints, encounter composition, objectives, rewards, and difficulty adaptation. Experiments across ten storylines show that the system produces distinct archetypal trajectories, improves entity diversity, and that Rise/Fall distinctions remain meaningful during play, aiding both narrative understanding and creator interpretation.
By Yunge Wen, Chenliang Huang, Hangyu Zhou, Zhuo Zeng, Yuxuan Weng, Timothy Merino, Julian Togelius, Max Kreminski, Sam Earle
arXiv:2601.15295v2 Announce Type: replace-cross
Abstract: Interactive narrative (IN) authors craft spaces of divergent narrative possibilities for players to explore, with the player's input determin...
By Yi Wang, John Joon Young Chung, Melissa Roemmele, Yuqian Sun, Tiffany Wang, Shm Garanganao Almeda, Brett A. Halperin, Yuwen Lu, Max Kreminski
arXiv:2608. 12336v1 Announce Type: cross Abstract: A story premise is the creative spark from which a full narrative can grow.
By Yang Yang, Zining Zhong, Qian Cao, Jindong Li, Boyun Xu, Kaishen Yuan, Menglin Yang, Yutao Yue
The paper introduces a framework that learns readerly dynamics—how readers respond over time—from high‑quality human stories and uses these dynamics to steer large language model (LLM) story generation. Instead of modeling full story content, a small LLM predicts low‑dimensional trajectories of future reader responses, which a larger, instruction‑following writer LLM then follows to produce story segments. Experiments show the approach improves trajectory‑based fidelity to human stories by 12%–35% while largely maintaining coherence.
By Danrui Li, Yiwei Zhao, Mubbasir Kapadia, Lingxiao Li
arXiv:2608. 08160v1 Announce Type: cross Abstract: The rapid advancement of Large Language Models (LLMs) is revolutionizing AI for Games by enabling open-ended and fluid interactive storytelling.
By Yingpeng Ma, Jianhao Yan, Bei Shi, Ka Hou Kam, Runnan Wang, Xuebo Liu, Yulong Chen, Yue Zhang, Derek F. Wong
arXiv:2605.22144v2 Announce Type: replace
Abstract: Existing approaches for digital short-drama production typically rely on one-shot LLM generated scripts and loosely coupled pipelines, which fail t...
By Yufei Shi, Weilong Yan, Naixuan Huang, Yucheng Chen, Chenyu Zhang, Tao He, Si Yong Yeo, Ming Li
arXiv:2608.29621v1 Announce Type: cross
Abstract: Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt con...
By Junxiang Liu, Lin Wang, Haiyu Shi, Hongxu Ma, Xiaoyu Yang, Chunjie Chen, Xiaoxiao Xu, Kaiqiao Zhan, Boao Wang, Shuizhou Shi, Tianyun Zhu, Jie Li, Jiangtong Li
arXiv:2608. 15654v1 Announce Type: cross Abstract: Large language models can write fluent stories, but open-ended storytelling requires more than local fluency.
By Yuqi Chen, Sixuan Li, Yunfeng Cai, Xueai Li, Ka Man Yan, Ying Li
arXiv:2606. 13348v1 Announce Type: cross Abstract: Computational creativity in Interactive Fiction faces a fundamental tension: Large Language Models (LLM) may produce creative narratives but struggle with world coherence, while symbolic systems ensure consistency but lack creative flexibility.
By Micaela Vaucher, Santiago Silveira, Santiago G\'ongora, Luis Chiruzzo
The House with a Million Windows (HWAMW) is an LLM-based interactive fiction system that lets users narrate a story and then view it through a series of AI-generated "windows" that reframe the narrative in various literary styles. The system is grounded in the psychological restorying intervention, aiming to deepen users' exploration of meaning in their personal stories. Empirical results indicate that HWAMW enhances users' sense of narrative identity, and expert reviews suggest it achieves this by facilitating restorying rather than simply generating new content.
By Cody Kommers, Sarah G Immel, Drew Hemment, Mina Lee