PEPR PRODIGE-IA
(2026-2030)

The rapid and impressive development of generative AI opens up transformative projects for several key sectors such as healthcare, education and industry. This evolution comes with major challenges in order to ensure safe, ethical, and effective development of these technologies. In particular, ethical issues such as plagiarism and the amplification of existing biases are currently paramount. Guarantees regarding the functioning of these generative models and the understanding of their underlying mechanisms remain preliminary. The PRODIGE-AI project aims to address three crucial issues for the development of safer, more efficient, and more transparent generative AI: the generalization capabilities of generative AI models, the efficiency and explainability of these AI models, and the development of geometric generative AI - in particular for graphs. These three areas represent topics where the state of the art requires new foundational frameworks to enable a controlled development. Our shared vision is that the promising potential of generative AI cannot be realized without a deep understanding of the fundamentals of generative deep neural networks and the mathematics underlying their learning mechanisms. Towards that endeavor, our consortium brings together both specialists in the foundations of machine learning and mathematicians experts in probability theory, graph theory and geometry. In mathematics, the project relies on expertise in stochastic processes, filtering, high dimensional statistics, random matrices, random graphs, random tensors, free probability, C*-algebras, graph theory, classical and quantum information theory, and optimal transport. Another important objective of the project is to structure a group of researchers at the frontier between the foundations of machine learning and probability theory to foster the development of innovative and relevant results for generative AI.