CHASE: How Content Ecosystems Are Reshaped When Ranking Is the Only Target

2026-08-31

AI Summary

研究通过CHASE框架模拟内容创作者反复适应LLM排名信号的过程，揭示了仅以排名为目标时内容生态如何变化及质量与排名一致性下降的问题。

Abstract

Generative Engine Optimization (GEO) is increasingly used to improve content visibility in LLM-based retrieval systems, yet its population-level effects under repeated optimization remain poorly understood. We introduce Content Homogenization under rAnking Signal Exploitation (CHASE), a controlled simulation framework for studying how content ecosystems are reshaped when creators repeatedly adapt documents to an LLM ranking signal. We use ranking as a proxy for source visibility and validate this abstraction against citations in grounded generated responses, obtaining a rank-citation AUC of 0.853 $\pm$ 0.093 across six domains. CHASE then iterates ranking, feature discrimination, rewriting, and evaluation over 20 rounds across different domains. Quality-ranking alignment decreases in all six domains: from R0 to R20, the change in Spearman's rho ranges from -0.107 to -0.018, with a mean change of -0.068, which means documents closer to the ranking feature profile become less aligned with independently judged document quality over the simulation horizon. A random-target control has shown that it is associated with adaptation toward ranking-derived incentives rather than iterative rewriting alone. The resulting ecosystem dynamics are strongly domain-dependent. Together, these findings show how repeated optimization against a fixed LLM ranking signal can reshape both content populations and the incentives faced by content creators.

Problem

• Content Homogenization
• Ranking Signal
• Generative Engine Optimization
• Ecosystem Dynamics
• Quality-Ranking Alignment

Innovation

• Generative Engine Optimization (GEO)
• Content Homogenization
• ranking signal exploitation
• Spearman's rho
• ecosystem dynamics