Breaking the Assistant Mold: Modeling Behavioral Variation in LLM Based Procedural Character Generation

• URL: http://arxiv.org/abs/2601.03396v1
• Date: Tue, 06 Jan 2026 20:18:01 GMT
• Title: Breaking the Assistant Mold: Modeling Behavioral Variation in LLM Based Procedural Character Generation
• Authors: Maan Qraitem, Kate Saenko, Bryan A. Plummer,
• Abstract summary: Procedural content generation has enabled vast virtual worlds through levels, maps, and quests, but large-scale character generation remains underexplored.<n>We identify two alignment-induced biases in existing methods.<n>We introduce PersonaWeaver, a framework that disentangles world-building from behavioral-building.
• Score: 62.54606886226136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Procedural content generation has enabled vast virtual worlds through levels, maps, and quests, but large-scale character generation remains underexplored. We identify two alignment-induced biases in existing methods: a positive moral bias, where characters uniformly adopt agreeable stances (e.g. always saying lying is bad), and a helpful assistant bias, where characters invariably answer questions directly (e.g. never refusing or deflecting). While such tendencies suit instruction-following systems, they suppress dramatic tension and yield predictable characters, stemming from maximum likelihood training and assistant fine-tuning. To address this, we introduce PersonaWeaver, a framework that disentangles world-building (roles, demographics) from behavioral-building (moral stances, interactional styles), yielding characters with more diverse reactions and moral stances, as well as second-order diversity in stylistic markers like length, tone, and punctuation. Code: https://github.com/mqraitem/Persona-Weaver

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