The evolution of search technology has decisively shifted from traditional rank-based models to an era dominated by Generative Search Engines, fundamentally changing how online content is discovered and consumed. This shift has rendered many existing SEO metrics and strategies obsolete, as they primarily optimize for keyword rankings and link authority rather than the nuanced demands of AI-driven content synthesis. To address this gap, researchers have introduced a pioneering framework called Generative Search Engine Optimization (GSEO), designed to understand, measure, and optimize how digital content influences AI-generated search answers. Traditional SEO focuses on enhancing visibility by optimizing for algorithms that rank pages based on factors like keyword frequency, site structure, backlinks, and user engagement. In contrast, Generative Search Engines synthesize information from various sources to produce concise, relevant answers rather than a ranked list, making conventional metrics inadequate for capturing true content impact in this context. The GSEO framework offers two main contributions to SEO research and practice. First, it introduces CC-GSEO-Bench, a large-scale, content-centric benchmark specifically crafted for GSEO evaluation. Unlike traditional attribution models that rely on surface signals such as clicks or keywords, this benchmark enables deep semantic evaluation of content, revealing how significantly particular pieces shape AI-generated answers. Second, the framework includes a novel multi-agent system that automates content refinement and optimization for generative search. This system employs a collaborative workflow—analyze, revise, and evaluate—where multiple agents assess content influence, propose enhancements, and test revisions, fostering scalable and systematic optimization suitable for evolving generative search landscapes. Empirical studies with this framework reveal valuable insights into content influence within generative search.

Minor adjustments in semantic content and presentation can markedly impact how much content informs synthesized responses. These findings provide actionable strategies for creators seeking to maintain visibility and authority amid AI-dominated search environments. Moreover, GSEO establishes a principled foundation for future research, equipping scholars and practitioners to better navigate the complexities of generative search engines. Overall, this advancement bridges the gap between traditional SEO and next-generation search technologies. As Generative Search Engines reshape information retrieval, frameworks like GSEO become essential for guiding content creators, marketers, and strategists toward more effective optimization methods. By adopting such innovations, stakeholders can ensure their content remains authoritative, influential, and discoverable in a landscape that prioritizes synthesized, context-rich answers over simple ranking. The introduction of CC-GSEO-Bench and the collaborative multi-agent system not only signifies leading-edge SEO research but also inaugurates a new discipline blending AI understanding, semantic analysis, and content strategy. As the digital ecosystem evolves, the ability to quantify and optimize meaningful content influence will be vital for maintaining competitive advantage in digital marketing and information dissemination. This comprehensive approach highlights the transformative potential of generative search and sets a new benchmark for SEO in the AI era.