Text-to-Stage: Spatial Layouts from Long-form Narratives

Jefferson Hernandez; Swarnadeep Saha; Chenxi Whitehouse; Sanjeel Parekh; Calvin Murdock; Yuliang Li; W. Owen Brimijoin; Vamsi Krishna Ithapu; Ishwarya Ananthabhotla

arXiv · 2603.17832

Text-to-Stage: Spatial Layouts from Long-form Narratives

Jefferson Hernandez·Swarnadeep Saha·Chenxi Whitehouse·Sanjeel Parekh·Calvin Murdock·Yuliang Li·W. Owen Brimijoin·Vamsi Krishna Ithapu·Ishwarya Ananthabhotla

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Abstract

In this work, we probe the ability of a language model to demonstrate spatial reasoning from unstructured text, mimicking human capabilities and automating a process that benefits many downstream media applications. Concretely, we study the narrative-to-play task: inferring stage-play layouts (scenes, speaker positions, movements, and room types) from text that lacks explicit spatial, positional, or relational cues. We then introduce a dramaturgy-inspired deterministic evaluation suite and, finally, a training and inference recipe that combines rejection SFT using Best-of-N sampling with RL from verifiable rewards via GRPO. Experiments on a text-only corpus of classical English literature demonstrate improvements over vanilla models across multiple metrics (character attribution, spatial plausibility, and movement economy), as well as alignment with an LLM-as-a-judge and subjective human preferences.

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BibTeX RIS

Jefferson Hernandez, Swarnadeep Saha, Chenxi Whitehouse, Sanjeel Parekh, Calvin Murdock, Yuliang Li, W. Owen Brimijoin, Vamsi Krishna Ithapu, Ishwarya Ananthabhotla. 2026-03-18. Text-to-Stage: Spatial Layouts from Long-form Narratives. https://arxiv.org/abs/2603.17832

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