Beyond Retrieval: Modeling Confidence Decay and Deterministic Agentic Platforms in Generative Engine Optimization

Researchers propose a new approach to Generative Engine Optimization (GEO) that moves beyond current RAG-based systems to deterministic multi-agent platforms. The study introduces mathematical models for confidence decay in LLMs and demonstrates near-zero hallucination rates through specialized agent routing in industrial applications.

• →Current GEO strategies using Retrieval-Augmented Generation suffer from probabilistic hallucinations and commercial trust issues.
• →Researchers developed Semantic Entropy Drift (SED) to mathematically model confidence decay in LLMs over time and context.
• →The proposed Deterministic Agent Handoff (DAH) protocol uses LLMs as intent routers rather than final answer generators.
• →Industrial validation with EasyNote AI product showed near-zero hallucination rates for specialized tasks.
• →The framework establishes theoretical foundations for next-generation human-AI collaboration ecosystems.