Beyond Keywords: Driving Generative Search Engine Optimization with Content-Centric Agents

2025-09-06

AI Summary

The rise of generative search engines has rendered traditional ranking-based SEO metrics obsolete, necessitating quantitative measurement of content’s semantic influence on synthesized answers. To address this, we propose CC-GSEO—the first content-centric generative search engine optimization framework—built upon a multi-agent system that enables an end-to-end “analyze–revise–evaluate” optimization loop. We design a multidimensional influence evaluation metric and release CC-GSEO-Bench, the first large-scale benchmark for generative SEO. Our approach integrates generative search analysis, semantic influence modeling, and automated iterative optimization. Experiments uncover key mechanisms by which content shapes synthetic answers, yielding interpretable and actionable optimization strategies for content creators. This work establishes the foundational methodology for systematic research in generative search optimization.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Constraint Satisfaction and Optimization: Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The paradigm shift from traditional ranked-based search to Generative Search Engines has rendered conventional SEO metrics obsolete, creating an urgent need to understand, measure, and optimize for content influence on synthesized answers. This paper introduces a comprehensive, end-to-end framework for Generative Search Engine Optimization (GSEO) to address this challenge. We make two primary contributions. First, we construct CC-GSEO-Bench, a large-scale, content-centric benchmark, and propose a multi-dimensional evaluation framework that systematically quantifies influence, moving beyond surface-level attribution to assess substantive semantic impact. Second, we design a novel multi-agent system that operationalizes this framework, automating the strategic refinement of content through a collaborative analyze-revise-evaluate workflow. Our empirical analysis using this framework reveals novel insights into the dynamics of content influence, offering actionable strategies for creators and establishing a principled foundation for future GSEO research.

Problem

• Optimizing content influence on generative search engine answers
• Developing a benchmark to quantify content's semantic impact
• Automating content refinement through multi-agent collaborative workflow

Innovation

• Content-centric multi-agent system automates refinement
• Multi-dimensional evaluation quantifies semantic impact
• Large-scale benchmark assesses content influence systematically