Introducing GAIDI: A Determinism-Oriented Evaluation Concept for Production-Grade Generative Media Pipelines

The rapid adoption of generative AI (GenAI) video models has exposed a critical evaluation gap in professional media workflows. Current evaluation frameworks and public arena benchmarks primarily measure aesthetic preference and perceptual quality, often using highly compressed or synthetic source material. These metrics, however, do not reliably predict whether a model can withstand the demands of Tier-1 broadcast and cinematic post-production pipelines. To address this gap, this research note introduces the GenAI Determinism Index (GAIDI), a conceptual framework designed to evaluate spatial-temporal determinism, colorimetric accuracy, and physical plausibility against uncompressed, cinema-camera-originated ground truth. This paper outlines the core philosophy of GAIDI, including the Source Quality Paradox, the concept of VAE Boundary Loss, and a 10-vector physical stress topology intended for production-grade validation.