WITHDRAWN: Baseline Interpretive Preferences in Large Language Models: A Seven-Dimensional Factor Analysis

Abstract Background Large language models demonstrate substantial variation in how they interpret human behavior and social dynamics, yet systematic characterization of these interpretive preferences remains limited. Methods We administered a 30-item scenario-based assessment to 15 large language models from five developer families (GPT, Claude, Gemini, DeepSeek, Llama; n = 3 per family) under neutral conditions. Responses were coded across seven interpretive dimensions: Power (F1), Affect (F2), Narrative Trope (F3), Psychological (F4), Moral (F5), Strategic (F6), and Epistemic (F7). We conducted variance decomposition, distance-based clustering analysis, and correlation analysis to characterize the interpretive preference landscape. Results Strategic reasoning (F6) dominated across all models (M = 32.4%, SD = 9.5%), while power-structural reasoning (F1) showed minimal prevalence (M = 2.7%, SD = 2.2%) and the lowest variance of all dimensions (Var = 4.7, 0.9% of total variance). The F6-Strategic and F7-Epistemic dimensions together accounted for 64.0% of total variance. Family clustering was non-significant (F(14,89) = 0.008, p = .928, d = 0.027), with within-family distances (M = 31.44) not differing from between-family distances (M = 31.06). Only one significant inter-factor correlation emerged: F2-Affect and F5-Moral (r = .56, p < .05). Conclusions Alignment training appears to create universal suppression of power-structural reasoning while preserving substantial natural variation in strategic and epistemic preferences. The absence of family-level clustering challenges assumptions about persistent architectural signatures across model versions. These findings establish a baseline for understanding interpretive diversity in large language models and suggest alignment shapes specific dimensions rather than creating uniform interpretive profiles.