Multiverse: Language-Conditioned Multi-Game Level Blending via Shared Representation

Computer Science > Artificial Intelligence [Submitted on 25 Mar 2026] Multiverse: Language-Conditioned Multi-Game Level Blending via Shared Representation In-Chang Baek, Jiyun Jung, Sung-Hyun Kim, Geum-Hwan Hwang, Kyung-Joong Kim Text-to-level generation aims to translate natural language descriptions into structured game levels, enabling intuitive control over procedural content generation. While prior text-to-level generators are typically limited to a single game domain, extending language-conditioned generation to multiple games requires learning representations that capture structural relationships across domains. We propose Multiverse, a language-conditioned multi-game level generator that enables cross-game level blending through textual specifications. The model learns a shared latent space aligning textual instructions and level structures, while a threshold-based multi-positive contrastive supervision links semantically related levels across games. This representation allows language to guide which structural characteristics should be preserved when combining content from different games, enabling controllable blending through latent interpolation and zero-shot generation from compositional textual prompts. Experiments show that the learned representation supports controllable cross-game level blending and significantly improves blending quality within the same game genre, while providing a unified representation for language-conditioned multi-game content generation. Comments: 8 pages, 5 figures, 4 tables Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.26782 [cs.AI]   (or arXiv:2603.26782v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.26782 Focus to learn more Submission history From: In-Chang Baek [view email] [v1] Wed, 25 Mar 2026 04:50:29 UTC (786 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)