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)