Art School for Machines: Process-Based World Model Knowledge Distillation as an Architectural Solution to Copyright Liability in Generative AI

Abstract

Contemporary generative AI systems for visual media are trained on datasets of completed works, creating structural copyright liability that no post-hoc mitigation can fully resolve. This paper proposes an alternative training architecture that eliminates copyright concerns at the pipeline's foundation rather than at its output. The proposed system uses a world model trained exclusively on recordings of artistic creation processes (technique demonstrations, instructional footage, studio recordings) to develop causal understanding of how visual art is physically produced. This process knowledge is then transferred via knowledge distillation into a text embedding model, which maps natural language prompts to technique-physics representations rather than aesthetic-pattern representations derived from copyrighted works. A downstream generative model conditioned on these embeddings produces visual outputs grounded in understanding of artistic method rather than statistical reproduction of existing works. The paper analyzes the copyright chain at each pipeline stage, demonstrating that no copyrightable expression enters the system at any point. The architecture is analogous to art education: the world model is the professor demonstrating technique, the embedding model is the student internalizing principles, and the generative model is the graduate creating original work. All component technologies (world models, knowledge distillation, embedding space alignment, generative architectures) exist independently in current literature; the contribution is their novel combination for the specific purpose of producing copyright-clean generative AI at the architectural level.