AI RESEARCH
Breaking the Assistant Mold: Modeling Behavioral Variation in LLM Based Procedural Character Generation
arXiv CS.CL
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ArXi:2601.03396v2 Announce Type: replace 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