Understanding the creation of artworks requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production. While recent generative AI systems synthesize artworks with high fidelity, they primarily model distributions over finished artifacts rather than the underlying creative processes.
Artistic workflows are often only partially documented through fragmented sources such as archival records, preparatory studies, and correspondence, making it difficult to formalize a process-level understanding computationally.
In this work, we introduce ArtMine, a framework for discovering and formalizing artistic processes from heterogeneous historical evidence. Our approach synthesizes heterogeneous artwork evidence into a structured repository, from which a Peircean abductive agent infers evidence-grounded production steps.
These steps are converted into a compositional graph and rendering prompt, then optimized through self-reflection over deviations between the generated and reference artworks.
We provide a preliminary proof-of-concept case study using open-domain historical sources across multiple artists and artistic movements, demonstrating that fragmented documentary evidence can support coherent, interpretable, and auditable representations of artistic workflows.
By modeling creative processes rather than just final artifacts, our work moves toward process-centered human-AI co-creativity systems that can support artistic interpretation, creative education, reflective collaboration, and computational studies of cultural production.
Blogger's Review: The introduction of ArtMine offers a fresh perspective on computational understanding of artistic creation processes, emphasizing the importance of extracting and formalizing processes from historical evidence. This not only aids academic research in the arts but also opens new avenues for human-AI collaboration, making it a topic worth exploring and studying further.