SOURCE-LINKED INTELLIGENCE

Agent2UCB: Agentic System for Generative Engine Optimization

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global