用智能代理自动优化内容，提升被生成引擎引用的概率。
Agent2UCB: Agentic System for Generative Engine Optimization
- 基于强化学习的智能体动态选择最优优化策略
- 在GEO-Bench上实现持续可见性提升，同时保持SEO质量
- 适合内容运营、数字营销人员快速迭代优化方案
以大语言模型驱动的搜索引擎（如 Google AI Overviews、Perplexity）催生了生成式引擎优化（GEO），即通过优化内容提高其被生成系统引用或摘要的可能性。本文提出 Agent2UCB，一个自主运行的GEO系统，通过定制化、反馈驱动的方式提升内容可见性。针对每个内容项，系统评估九种GEO策略，利用基于多臂赌博机的Agent2UCB策略，融合LLM先验与在线奖励信号，加速有效方法的选择。为监控副作用，系统还提供轻量级、仅文本的SEO就绪评估，涵盖可读性、主题覆盖度及EEAT风格可信度。在GEO-Bench上的实验表明，该系统能持续提升可见性，同时维持SEO质量。演示支持用户自选网站，观察优化流程，并对比不同方法的GEO/SEO效果。
原文摘要 · Abstract (English)
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.
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