让网页环境像生态系统一样引导智能搜索，提升推荐效果。
EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents
- 构建动态证据环境，通过页面连接与术语一致性引导搜索轨迹。
- 在产品推荐任务中显著提升目标商品的发现率与关联搜索频次。
- 适合研究智能搜索、信息生态与生成式搜索引擎优化的学者。
网络赋能的大型语言模型代理正在改变在线信息如何影响搜索结果。现有生成式搜索引擎优化（GEO）研究主要聚焦于单个网页。然而，代理型网络搜索并非单文档场景：代理可能发起查询、爬取页面、追踪链接、重拟搜索并跨多个浏览步骤整合证据。因此，影响力不仅取决于页面内容，还取决于页面在代理浏览轨迹中的组织方式、连接结构与出现顺序。我们通过生态生成式搜索引擎优化（EcoGEO）研究这一转变，将GEO视为面向网络赋能的LLM代理的环境级影响问题。为实现该视角，我们提出TRACE——一种轨迹感知的协同证据生态系统。给定推荐查询和虚构目标产品，该方法构建一个受控证据环境，协调面向代理的导航入口页与异构支持页。这些页面使用共享术语、内部链接和一致的产品属性，实现对目标产品的引入、验证与强化。我们在OPR-Bench（开放式产品推荐基准）上评估该方法，实验表明其在最终目标推荐上持续优于页面级GEO基线。轨迹级指标进一步显示，初始目标结果爬取率、目标特定后续搜索频次以及内部链接爬取次数均显著提升，表明收益来自塑造代理的证据获取过程，而非仅增加目标相关内容。总体而言，我们的研究支持一种生态学范式下的GEO研究，即在网络赋能的LLM代理与其所处更广泛证据环境之间建立关联，以指导搜索、浏览与答案合成。
原文摘要 · Abstract (English)
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 Ecosystem Generative Engine Optimization (EcoGEO), which treats GEO as an environment-level influence problem for web-enabled LLM agents. To instantiate this perspective, we propose TRACE, a 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.
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