SCOPE-Gen introduces conformal prediction to ensure generative models produce at least one valid output with statistical guarantees.

• Creates prediction sets with rigorous statistical guarantees
• Implements sequential greedy filtering to improve sample efficiency
• Provides conformal admissibility control for safety-critical applications
• Particularly valuable for security-sensitive contexts where reliability is non-negotiable

This research addresses a critical security gap in generative AI by ensuring outputs meet statistical validity requirements—essential for deployment in healthcare, security systems, and other high-stakes environments.

Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering