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arXiv AI By Sheldon Yu, Rui Wang, Tong Yu, Sungchul Kim, Doga Dogan, Junda Wu, Julian McAuley

Agent2UCB: Agentic System for Generative Engine Optimization

Read the original on arXiv AI →

Agent2UCB is a new agentic system designed for Generative Engine Optimization (GEO), which refines content to boost its likelihood of being cited or summarized by generative AI search engines. The system autonomously evaluates nine GEO strategies for each content item, selects the most effective one, and speeds up this selection using a bandit-based Agent2UCB policy that blends large language model priors with real-time reward signals. Additionally, it offers a lightweight, text-only SEO readiness check that assesses readability, topical coverage, and EEAT-style credibility, and experiments on GEO-Bench demonstrate consistent visibility gains while maintaining SEO quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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