This research introduces a minimum entropy principle to overcome fundamental limitations in current generative AI technologies.

• Addresses the plateau in GenAI progress despite initial exponential growth
• Tackles key challenges: data-hunger, overfitting, and difficult model control
• Presents a mathematical framework to improve generative efficiency
• Demonstrates practical effectiveness in image generation applications

Medical Impact: This approach could significantly enhance AI algorithms for medical drug design by reducing data requirements and improving directability—critical factors for reliable, efficient healthcare applications where data may be limited and precision is essential.

Breaking the bonds of generative artificial intelligence by minimizing the maximum entropy