Agent2UCB: Agentic System for Generative Engine Optimization

2026-08-29

AI Summary

为提高内容在生成系统中的可见性，本文提出Agent2UCB系统，通过评估九种优化策略并采用基于Bandit的算法选择最优方案，同时监控SEO质量。

Abstract

Large language model driven search engines such as Google AI Overviews and Perplexity have created new opportunities for Generative Engine Optimization (GEO) the practice of refining content to increase its likelihood of being cited or summarized by generative systems. We demonstrate Agent2UCB, an agentic GEO system that autonomously improves content visibility through customized, feedback-driven optimization. For each content item, the system evaluates nine GEO strategies, identifies the most effective method, and accelerates selection using a bandit-based Agent2UCB policy that integrates LLM priors with online reward signals. To monitor side effects, the system also provides a lightweight, text-only SEO readiness evaluation covering readability, topical coverage, and EEAT-style credibility. Experiments on GEO-Bench show consistent visibility gains while preserving SEO quality. The demo allows users to choose the websites of interest, observe the optimization workflow, and compare GEO/SEO outcomes across methods.

Problem

• Generative Engine Optimization
• Content Visibility
• LLM
• GEO Strategies
• Feedback-Driven Optimization

Innovation

• Agent2UCB
• Generative Engine Optimization
• Bandit-Based Policy
• LLM Priors
• SEO Readiness Evaluation