Applications of Information-Theoretic Concepts for Generative AI Systems

Dear Colleagues,

Generative Artificial Intelligence (GenAI) has rapidly transformed domains ranging from natural language processing and computer vision to computational creativity. However, its design, optimization, and evaluation present unique challenges—particularly in understanding and controlling uncertainty, bias, and interpretability. Information theory offers a rigorous mathematical framework for addressing these challenges, providing quantifiable entities such as entropy, mutual information, and transfer entropy to analyze, optimize, and interpret generative models.

This Special Issue aims to highlight contributions addressing theoretical advances, computational techniques, and practical applications of information-theoretic concepts in the development, evaluation, and deployment of generative AI systems. Topics of interest include but are not limited to information-theoretic training objectives, causal inference in generative models, rate–distortion theory for compression in AI pipelines, information bottleneck approaches, uncertainty quantification, and the use of transfer entropy for interpretability.

We invite contributions from academia and industry that span theory, algorithms, and real-world applications, fostering a multidisciplinary dialogue to advance the synergy between information theory and next-generation generative AI systems.

Dr. Deniz Gençağa
Dr. Rita Singh
Guest Editors

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