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This research addresses the core tension in generative AI by combining Large Language Models with symbolic logic to create coherent interactive fiction worlds, offering a scalable path toward consistent computational creativity. The proposed neuro-symbolic framework demonstrates that grounding LLM outputs can maintain narrative flexibility while ensuring structural integrity and puzzle solvability within complex environments. While current limitations regarding constraint bypassing remain, the study provides critical design principles for future systems aiming to balance generative freedom with logical consistency in AI-driven storytelling applications.
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IVIE: A Neuro-symbolic Approach to Incremental and Validated Generation of Interactive Fiction Worlds

arXiv3 min read

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. We present IVIE (Incremental & Validated Interactive Experiences), a neuro-symbolic approach to generating complete and playable interactive fiction worlds from scratch. Building upon PAYADOR's neuro-symbolic framework, IVIE implements a four-stage incremental generation pipeline that delegates creative decisions--setting and character creation, puzzle design--to LLMs while grounding the world state through symbolic validation. The system generates worlds with interconnected locations, functional items, non-player characters, and coherent puzzles, all structured around a central goal-oriented architecture. Human evaluation shows the approach generates immersive, thematically coherent worlds with high player engagement. Results seem to indicate that the neuro-symbolic approach successfully balances flexibility with narrative coherence: symbolic validation grounds LLM generation without eliminating generative freedom. However, challenges remain: LLM inconsistencies occasionally bypass puzzle constraints, and objective validation gaps allow some structurally impossible goals. We identify key design considerations for future neurosymbolic interactive storytelling systems, particularly regarding LLM capabilities and their limitations.

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. We present IVIE (Incremental & Validated Interactive Experiences), a neuro-symbolic approach to generating complete and playable interactive fiction worlds from scratch. Building upon PAYADOR's neuro-symbolic framework, IVIE implements a four-stage incremental generation pipeline that delegates creative decisions--setting and character creation, puzzle design--to LLMs while grounding the world state through symbolic validation. The system generates worlds with interconnected locations, functional items, non-player characters, and coherent puzzles, all structured around a central goal-oriented architecture. Human evaluation shows the approach generates immersive, thematically coherent worlds with high player engagement. Results seem to indicate that the neuro-symbolic approach successfully balances flexibility with narrative coherence: symbolic validation grounds LLM generation without eliminating generative freedom. However, challenges remain: LLM inconsistencies occasionally bypass puzzle constraints, and objective validation gaps allow some structurally impossible goals. We identify key design considerations for future neurosymbolic interactive storytelling systems, particularly regarding LLM capabilities and their limitations.

10 pages, 3 figures. To appear in the Proceedings of the 16th International Conference on Computational Creativity (ICCC'26), June 2026

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2606.13348 [cs.CL]

(or arXiv:2606.13348v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.13348 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 294 words

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