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single-document setting: an agent may issue queries, crawl pages, follow links, reformulate searches, and synthesize evidence across multiple browsing steps. \\ Influence therefore depends not only on page content, but also on how pages are organized, connected, and encountered along the agent's browsing trajectory. \\ We study this shift through \\textbf{Ecosystem Generative Engine Optimization} (\\textbf{EcoGEO}), which treats GEO as an environment-level influence problem for web-enabled LLM agents. \\ To instantiate this perspective, we propose \\textbf{TRACE}, a \\textbf{Trajectory-Aware Coordinated Evidence Ecosystem}. \\ Given a recommendation query and a fictional target product, our method builds a controlled evidence en","title":"EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents","url":"https://arxiv.org/abs/2605.12887","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.12887v1 Announce Type: cross \nAbstract: Web-enabled LLM agents are changing how 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