SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization

• URL: http://arxiv.org/abs/2602.12187v1
• Date: Thu, 12 Feb 2026 17:18:00 GMT
• Title: SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization
• Authors: Sunghwan Kim, Wooseok Jeong, Serin Kim, Sangam Lee, Dongha Lee,
• Abstract summary: Search-Augmented Generative Engines (SAGE) have emerged as a new paradigm for information access.<n>No evaluation environment currently supports comprehensive investigation of SAGEO.<n>We introduce SAGEO Arena, a realistic and reproducible environment for stage-level SAGEO analysis.
• Score: 11.467565046589414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Search-Augmented Generative Engines (SAGE) have emerged as a new paradigm for information access, bridging web-scale retrieval with generative capabilities to deliver synthesized answers. This shift has fundamentally reshaped how web content gains exposure online, giving rise to Search-Augmented Generative Engine Optimization (SAGEO), the practice of optimizing web documents to improve their visibility in AI-generated responses. Despite growing interest, no evaluation environment currently supports comprehensive investigation of SAGEO. Specifically, existing benchmarks lack end-to-end visibility evaluation of optimization strategies, operating on pre-determined candidate documents that abstract away retrieval and reranking preceding generation. Moreover, existing benchmarks discard structural information (e.g., schema markup) present in real web documents, overlooking the rich signals that search systems actively leverage in practice. Motivated by these gaps, we introduce SAGEO Arena, a realistic and reproducible environment for stage-level SAGEO analysis. Our objective is to jointly target search-oriented optimization (SEO) and generation-centric optimization (GEO). To achieve this, we integrate a full generative search pipeline over a large-scale corpus of web documents with rich structural information. Our findings reveal that existing approaches remain largely impractical under realistic conditions and often degrade performance in retrieval and reranking. We also find that structural information helps mitigate these limitations, and that effective SAGEO requires tailoring optimization to each pipeline stage. Overall, our benchmark paves the way for realistic SAGEO evaluation and optimization beyond simplified settings.

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