[2603.20213] AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization

Computer Science > Artificial Intelligence arXiv:2603.20213 (cs) [Submitted on 2 Mar 2026] Title:AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization Authors:Jiaqi Yuan, Jialu Wang, Zihan Wang, Qingyun Sun, Ruijie Wang, Jianxin Li View a PDF of the paper titled AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization, by Jiaqi Yuan and 5 other authors View PDF HTML (experimental) Abstract:Generative search engines represent a transition from traditional ranking-based retrieval to Large Language Model (LLM)-based synthesis, transforming optimization goals from ranking prominence towards content inclusion. Generative Engine Optimization (GEO), specifically, aims to maximize visibility and attribution in black-box summarized outputs by strategically manipulating source content. However, existing methods rely on static heuristics, single-prompt optimization, or engine preference rule distillation that is prone to overfitting. They cannot flexibly adapt to diverse content or the changing behaviors of generative engines. Moreover, effectively optimizing these strategies requires an impractical amount of interaction feedback from the engines. To address these challenges, we propose AgenticGEO, a self-evolving agentic framework formulating optimization as a content-conditioned control problem, which enhances intrinsic content quality to robustly adapt to the unpredictable behaviors of black-box engines. Unlike fixed-strategy methods, A...