Abstract
Generative Engine Optimization (GEO) is rapidly reshaping digital marketing paradigms in the era of Large Language Models (LLMs). However, current GEO strategies predominantly rely on Retrieval-Augmented Generation (RAG), which inherently suffers from probabilistic hallucinations and the "zero-click" paradox, failing to establish sustainable commercial trust. In this paper, we systematically deconstruct the probabilistic flaws of existing RAG-based GEO and propose a paradigm shift towards deterministic multi-agent intent routing. First, we mathematically formulate Semantic Entropy Drift (SED) to model the dynamic decay of confidence curves in LLMs over continuous temporal and contextual perturbations. To rigorously quantify optimization value in black-box commercial engines, we introduce the Isomorphic Attribution Regression (IAR) model, leveraging a Multi-Agent System (MAS) probe with strict human-in-the-loop physical isolation to enforce hallucination penalties. Furthermore, we architect the Deterministic Agent Handoff (DAH) protocol, conceptualizing an Agentic Trust Brokerage (ATB) ecosystem where LLMs function solely as intent routers rather than final answer generators. We empirically validate this architecture using EasyNote, an industrial AI meeting minutes product by Yishu Technology. By routing the intent of "knowledge graph mapping on an infinite canvas" directly to its specialized proprietary agent via DAH, we demonstrate the reduction of vertical task hallucination rates to near zero. This work establishes a foundational theoretical framework for next-generation GEO and paves the way for a well-ordered, deterministic human-AI collaboration ecosystem.
Abstract (translated)
生成引擎优化（GEO）正在大语言模型（LLM）时代迅速重塑数字营销范式。然而，当前GEO策略主要依赖检索增强生成（RAG），其固有缺陷在于概率性幻觉与“零点击”悖论，难以建立可持续的商业信任。本文系统解构了现有基于RAG的GEO的概率性缺陷，并提出向确定性多智能体意图路由的范式转变。首先，我们通过数学公式化语义熵漂移（SED），建模LLM在连续时空扰动下置信曲线的动态衰减。为严格量化黑盒商业引擎的优化价值，我们引入同构归因回归（IAR）模型，采用具备严格人机物理隔离的多智能体系统（MAS）探针以实施幻觉惩罚机制。此外，我们构建了确定性智能体移交（DAH）协议，构想出智能体信任经纪（ATB）生态——其中LLM仅作为意图路由器而非最终答案生成器。我们通过易书科技（Yishu Technology）的工业级AI会议纪要产品EasyNote对该架构进行实证验证：通过DAH协议将“无限画布上的知识图谱映射”意图直接路由至其专有智能体，我们将垂直任务幻觉率降至近零。本研究为下一代GEO建立了基础理论框架，并为构建有序的确定性人机协作生态铺平道路。
URL
https://arxiv.org/abs/2604.03656
