This research introduces a probabilistic framework to improve reliability and reduce errors in compound LLM systems like cascades.

• Addresses the challenge of predicting performance in interconnected LLM systems
• Provides mathematical models to understand how errors propagate in LLM cascades
• Enables more accurate confidence assessment, reducing hallucination risks
• Creates a foundation for building more secure AI systems with predictable behavior

Security Impact: By enabling better prediction and management of error rates across connected LLMs, organizations can deploy more trustworthy AI systems for sensitive applications where reliability is critical.

Rational Tuning of LLM Cascades via Probabilistic Modeling