Title: Formal Verification of Generative Models

Abstract

This paper presents a novel framework for formal verification of generative models, focusing on ensuring their stability and preventing the generation of undesirable outputs. Generative models, such as GANs and diffusion models, are increasingly prevalent in various applications, yet their inherent complexity makes them challenging to verify formally. This research introduces a new system that analyzes probabilistic dependencies within these models, identifies potential failure modes, and constructs a formal verification protocol. The core mechanism involves creating a formal system capable of systematically assessing the model's behavior under various conditions, thereby guaranteeing convergence and mitigating risks associated with unexpected outputs. The paper details the system's architecture, illustrates its application with a specific example, and discusses its potential impact on the field of generative AI.