Deep Research Engine: Multi-LLM Talent Discovery from Facial Personality Analysis

DEEP RESEARCH ENGINE: MULTI-LLM TALENT DISCOVERY FROM FACIAL PERSONALITY ANALYSIS PROBLEM & SIGNIFICANCE Traditional talent assessment in children costs 500-2, 000 per child and requires 2-3 hours of expert time, limiting access to privileged populations. This preprint presents a two-stage AI system achieving expert-level accuracy at 0. 041 per analysis. INNOVATION Stage 1: Deep Research Engine extracts 137 personality traits from facial photographs using gradient boosting ensemble (CatBoost 40%, XGBoost 35%, LightGBM 25%). Stage 2: Multi-LLM Talent Analyzer processes traits through 5-25 parallel LLM agents from diverse providers (OpenAI, Anthropic, Gemini, XAI) for comprehensive talent profiling across 306 categories. KEY RESULTS - Internal Validation: AUC 0. 81 95% CI: 0. 78-0. 84 - Human Expert Comparison: AI r=0. 351 vs. Experts r=0. 291 (+6. 0%, p=0. 46) - Equal-Feature Baseline: Facial AUC=0. 82 vs. Questionnaire AUC=0. 54 - Cost Efficiency: 0. 041 per analysis (10, 000× reduction) - External Validation: Banking AUC 0. 94 (N>5, 000)