Agent2UCB：用于生成式引擎优化的智能系统
Agent2UCB: Agentic System for Generative Engine Optimization
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- UC San Diego（加州大学圣地亚哥分校）
- Adobe Research（奥多比研究院）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
该研究提出智能GEO系统Agent2UCB，通过评估9种策略并结合多臂老虎机策略优化内容，在GEO-Bench实验中实现可见性提升且保留SEO质量，还提供SEO评估与演示功能。
中文摘要 AI 辅助
谷歌AI概览和Perplexity等大型语言模型驱动的搜索引擎为生成式引擎优化（GEO，即优化内容以提高其被生成系统引用或摘要的可能性的实践）创造了新机遇。我们展示了Agent2UCB，一种自主通过定制化、反馈驱动的优化提升内容可见性的智能GEO系统。针对每个内容项，该系统评估9种GEO策略，识别最有效方法，并利用结合了LLM先验知识与在线奖励信号的基于多臂老虎机的Agent2UCB策略加速选择。为监测副作用，该系统还提供轻量级纯文本SEO就绪度评估，涵盖可读性、主题覆盖及EEAT式可信度。在GEO-Bench上的实验显示，该系统在保持SEO质量的同时实现了持续的可见性提升。演示允许用户选择感兴趣的网站、观察优化工作流并比较不同方法的GEO/SEO结果。
英文摘要
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.
