FeatGEO Framework Optimizes Generative Answer Engine Citation Visibility Through Feature-Level Multi-Objective Approach

The newly developed optimization framework, FeatGEO, tackles the visibility issues posed by generative answer engines, which utilize selective citation instead of the traditional ranked retrieval method. This evolution necessitates innovative optimization strategies that go beyond standard search engine optimization techniques. Current methods for optimizing generative engines often rely on rewriting text at the token level, resulting in limited interpretability and inadequate control over the balance between citation visibility and content quality. FeatGEO offers a multi-objective optimization framework at the feature level, abstracting webpages into understandable structural, content, and linguistic attributes. Rather than modifying text directly, it optimizes within this feature space and uses a language model to convert feature configurations into natural language. Experiments on GEO-Bench with three generative engines demonstrate that FeatGEO consistently enhances performance. The research paper, titled "Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility," is accessible on arXiv under the identifier arXiv:2604.19113v1 and falls under the category of cross announcement, reflecting the changing dynamics of content discovery and visibility in AI-driven information systems.