Dual-Track Scoring: How Geometry Anchors AI to Reliable Face Ratings

LLM-based scoring suffers from inherent variance that degrades user experience when consistency is expected. This dual-track approach—pairing LLM aesthetic judgment with deterministic geometric features—demonstrates how hybrid systems can preserve AI's pattern recognition while ensuring mathematical reliability. As AI moves into production environments, anchoring learned models with verifiable measurements may become essential for maintaining user trust and explainability.