Generative answer engines expose content through selective citation rather than ranked retrieval, fundamentally altering how visibility is determined. This shift calls for new optimization methods beyond traditional search engine optimization. Existing generative engine optimization (GEO) approaches primarily rely on token-level text rewriting, offering limited interpretability and weak control over the trade-off between citation visibility and content quality. We propose FeatGEO, a feature-level, multi-objective optimization framework that abstracts webpages into interpretable structural, content, and linguistic properties. Instead of directly editing text, FeatGEO optimizes over this feature space and uses a language model to realize feature configurations into natural language, decoupling high-level optimization from surface-level generation. Experiments on GEO-Bench across three generative engines demonstrate that FeatGEO consistently improves citation visibility while maintaining or improving content quality, substantially outperforming token-level baselines. Further analyses show that citation behavior is more strongly influenced by document-level content properties than by isolated lexical edits, and that the learned feature configurations generalize across language models of different scales.

翻译：生成式答案引擎通过选择性引用而非排序检索来呈现内容，从根本上改变了可见性的确定方式。这一转变要求超越传统搜索引擎优化的全新优化方法。现有生成式引擎优化（GEO）方法主要依赖词元级文本改写，可解释性有限，且对引文可见性与内容质量之间权衡的控制力薄弱。我们提出FeatGEO——一种特征级多目标优化框架，将网页抽象为可解释的结构、语义与语言属性。FeatGEO不直接编辑文本，而是在该特征空间中实施优化，并利用语言模型将特征配置转化为自然语言，从而解耦高层优化与表层生成。在GEO-Bench基准上针对三种生成式引擎的实验表明：FeatGEO在维持或提升内容质量的同时，持续提升了引文可见性，显著优于词元级基线方法。进一步分析揭示：引文行为受文档级内容属性的影响远大于孤立词汇编辑，且学习到的特征配置可跨不同规模的语言模型泛化。