arXiv:2605. 12887v2 Announce Type: replace-cross Abstract: Web-enabled LLM agents are changing how online information influences search outcomes.
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.
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.
arXiv:2608. 16824v1 Announce Type: new Abstract: Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines.
arXiv:2607. 17719v2 Announce Type: replace Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
arXiv:2607. 08662v1 Announce Type: cross Abstract: Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks.
arXiv:2607. 17719v1 Announce Type: new Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost.