EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents

2026-05-12

AI Summary

Existing Generative Engine Optimization (GEO) approaches are confined to single-page optimization and overlook the dynamic process by which LLM agents gather and integrate evidence across multi-step browsing trajectories in web environments. This work proposes EcoGEO, a novel framework that models GEO from an ecosystem perspective, introducing TRACE—a trajectory-aware coordinated evidence system. TRACE jointly designs entry and supporting pages through shared terminology, internal linking, and consistent product attributes to guide agent search paths. By moving beyond traditional page-level optimization, EcoGEO significantly improves target product recommendation rates on OPR-Bench and enhances key trajectory-level metrics, including target-relevant crawling, follow-up searches, and internal link clicks.

Abstract

Web-enabled LLM agents are changing how online information influences search outcomes. \ Existing Generative Engine Optimization (GEO) studies mainly focus on individual webpages. \ However, agentic web search is not a 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 environment that coordinates an agent-facing navigation entry page with heterogeneous support pages. \ These pages use shared terminology, internal links, and consistent product attributes to introduce, verify, and reinforce the target product. We evaluate our method on OPR-Bench, a benchmark for open-ended product recommendation. \ Experiments show that it consistently outperforms page-level GEO baselines in final target recommendation. \ Trajectory-level metrics further show increased initial target-result crawls, target-specific follow-up searches, and internal-link crawls, suggesting that the gains come from shaping the agent's evidence-acquisition process rather than merely adding more target-related content. \ Overall, our findings support an ecosystem research paradigm for GEO, where web-enabled LLM agents are studied in relation to the broader evidence environments that guide search, browsing, and answer synthesis.

Problem

• Generative Engine Optimization
• Web-enabled LLM agents
• browsing trajectory
• evidence ecosystem
• search influence

Innovation

• EcoGEO
• trajectory-aware
• evidence ecosystem
• web-enabled LLM agents
• Generative Engine Optimization