分类,文件名,论文题名,年份,页数,文件大小MB,来源,URL,备注
01_GEO基础框架,01_GEO基础框架_GEO_Generative_Engine_Optimization.pdf,GEO: Generative Engine Optimization,2023/2024,12,1.35,"arXiv, KDD 2024",https://arxiv.org/pdf/2311.09735,GEO 概念和 GEO-bench 的奠基论文
01_GEO基础框架,01_GEO基础框架_GEO_in_Digital_Repositories.pdf,Generative Engine Optimization in Digital Repositories: Optimizing Visibility for Generative AI,2025,25,0.53,Infonomy,https://infonomy.scimagoepi.com/index.php/infonomy/en/article/download/115/153/217,数字知识库中的 GEO 应用
01_GEO基础框架,01_GEO基础框架_How_to_Dominate_AI_Search.pdf,Generative Engine Optimization: How to Dominate AI Search,2025,27,14.61,arXiv,https://arxiv.org/pdf/2509.08919,面向 AI Search 可见性的综合框架
01_GEO基础框架,01_GEO基础框架_Navigating_the_Shift_Web_Search_vs_Generative_AI_Response.pdf,Navigating the Shift: A Comparative Analysis of Web Search and Generative AI Response Generation,2026,4,1.75,arXiv,https://arxiv.org/pdf/2601.16858,传统搜索与生成式回答的对比分析
02_GEO方法优化,02_GEO方法优化_Beyond_Keywords_Content_Centric_Agents_GSEO.pdf,Beyond Keywords: Driving Generative Search Engine Optimization with Content-Centric Agents,2025,16,0.75,arXiv,https://arxiv.org/pdf/2509.05607,内容中心代理驱动的 GSEO 方法
02_GEO方法优化,02_GEO方法优化_Beyond_SEO_Transformer_Content_Optimisation.pdf,Beyond SEO: A Transformer-Based Approach for Reinventing Web Content Optimisation,2025,9,0.64,arXiv mirror,https://arxiv.org/pdf/2507.03169,Transformer 辅助网页内容优化
02_GEO方法优化,02_GEO方法优化_IF_GEO_Conflict_Aware_Instruction_Fusion.pdf,IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimization,2026,15,1.34,arXiv,https://arxiv.org/pdf/2601.13938,多查询场景中的冲突感知指令融合
02_GEO方法优化,02_GEO方法优化_Role_Augmented_Intent_Driven_GSEO.pdf,Role-Augmented Intent-Driven Generative Search Engine Optimization,2025,15,1.73,arXiv,https://arxiv.org/pdf/2508.11158,角色增强和意图驱动的优化
02_GEO方法优化,02_GEO方法优化_Think_Before_Writing_Feature_Level_Optimization.pdf,Think Before Writing: Feature-Level Optimization for Generative Engine Optimization,2026,14,0.69,arXiv,https://arxiv.org/pdf/2604.19113,特征级内容优化方法
02_GEO方法优化,02_GEO方法优化_What_Generative_Search_Engines_Like.pdf,What Generative Search Engines Like: Explaining and Optimizing Website Visibility in Generative Search,2025,30,1.17,arXiv,https://arxiv.org/pdf/2510.11438,解释生成式搜索偏好的可见性研究
02_GEO方法优化,02_GEO方法优化_White_Hat_Search_Engine_Optimization_using_LLMs.pdf,White Hat Search Engine Optimization using Large Language Models,2025,5,0.15,arXiv,https://arxiv.org/pdf/2502.07315,白帽 SEO 与 LLM 辅助内容优化
03_GEO测量评估,03_GEO测量评估_AI_Answer_Engine_Citation_Behavior_GEO16.pdf,AI Answer Engine Citation Behavior: A Longitudinal Study of GEO in 16 Generative Engines,2025,12,2.11,arXiv,https://arxiv.org/pdf/2509.10762,16 个生成式引擎的引用行为纵向研究
03_GEO测量评估,03_GEO测量评估_C_SEO_Bench_Does_Conversational_SEO_Work.pdf,C-SEO Bench: Does Conversational SEO Work?,2025,18,0.90,"arXiv, NeurIPS Datasets and Benchmarks",https://arxiv.org/pdf/2506.11097,会话式 SEO 的评估基准
03_GEO测量评估,03_GEO测量评估_Dont_Measure_Once_AI_Search_Visibility.pdf,Don’t Measure Once: Reliable AI Search Visibility Requires Repeated Trials and Structure-Aware Metrics,2026,19,1.04,arXiv,https://arxiv.org/pdf/2604.07585,AI 搜索可见性测量的重复试验和结构指标
03_GEO测量评估,03_GEO测量评估_From_Citation_Selection_to_Citation_Absorption.pdf,From Citation Selection to Citation Absorption: Understanding Source Influence in LLM-Generated Answers,2026,27,1.01,arXiv,https://arxiv.org/pdf/2604.25707,从引用选择到内容吸收的源影响分析
03_GEO测量评估,03_GEO测量评估_SAGEO_Arena_Realistic_Environment.pdf,SAGEO: A Search Arena for Generative Engine Optimization in a Realistic Environment,2026,12,2.54,arXiv,https://arxiv.org/pdf/2602.12187,更接近真实环境的 GEO 竞技场
03_GEO测量评估,03_GEO测量评估_Structural_Feature_Engineering_for_GEO.pdf,Structural Feature Engineering for Generative Engine Optimization,2026,10,0.92,arXiv,https://arxiv.org/pdf/2603.29979,结构特征工程对 GEO 的影响
04_AI搜索实证,04_AI搜索实证_A_Survey_of_Generative_Search_and_Recommendation.pdf,A Survey of Generative Search and Recommendation in the Era of Large Language Models,2024/2025,19,1.01,Generative Search and Recommendation,https://generative-rec.github.io/assets/files/survey.pdf,生成式搜索与推荐综述
04_AI搜索实证,04_AI搜索实证_How_Generative_AI_Disrupts_Search_Google_AI_Overviews.pdf,"How Generative AI Disrupts Search: Consumer Behavior, Website Traffic, and the Impact of Google AI Overviews",2026,12,2.20,ACM SIGIR 2026 author PDF,https://web.njit.edu/~borcea/papers/acm-sigir26.pdf,AI Overviews 对用户行为和流量的影响
04_AI搜索实证,04_AI搜索实证_NExT_Search_User_Feedback_Ecosystem.pdf,NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search,2025,10,0.97,arXiv,https://arxiv.org/pdf/2505.14680,生成式 AI 搜索中的用户反馈生态
04_AI搜索实证,04_AI搜索实证_What_Evidence_Do_Language_Models_Find_Convincing.pdf,What Evidence Do Language Models Find Convincing?,2024,17,0.91,arXiv,https://arxiv.org/pdf/2402.11782,语言模型对证据的偏好
05_AEO理论整合,05_AEO理论整合_From_SEO_to_AEO_Generative_AI_Search_Visibility.pdf,From SEO to Answer Engine Optimization (AEO): Generative AI and the Transformation of Search Visibility,2025,18,0.34,ResearchGate chapter PDF,https://www.researchgate.net/publication/399872181_From_SEO_to_Answer_Engine_Optimization_AEO_Generative_Ai_and_the_Transformation_of_Search_Visibility,AEO 与搜索可见性转型
05_AEO理论整合,05_AEO理论整合_Integrated_Framework_for_SEO_GEO_AEO.pdf,"Optimizing for the Artificial Intelligence-Driven Search Era: An Integrated Framework for SEO, GEO, and AEO",2025,10,0.44,IJSREM,https://ijsrem.com/uploads/production/Optimizing-for-the-Artificial-Intelligence-Driven-Search-Era-An-Integrated-Framework-for-Search-Engine-Optimization-Generative-Engine-Optimization-and-Answer-Engine-Optimization.pdf,SEO、GEO、AEO 的整合框架
05_AEO理论整合,05_AEO理论整合_Smart_Search_Optimization_SEO_AEO_GEO.pdf,"Smart Search Optimization: A Theoretical Framework Integrating SEO, AEO and GEO in the Era of Generative AI",2025,10,0.32,Loro Journal,https://lorojournals.com/index.php/emsj/en/article/download/1728/1679/2401,SEO、AEO、GEO 的理论整合
05_AEO理论整合,05_AEO理论整合_Transition_from_SEO_to_GEO_AEO_AIO_Digital_Marketing.pdf,"How the Transition from SEO to GEO, AEO, and AIO is Impacting Digital Marketing Strategies",2025,9,0.54,IJCRT,https://www.ijcrt.org/papers/IJCRT2510315.pdf,数字营销策略视角下的转型
05_AEO理论整合,05_AEO理论整合_Zero_Click_Search_and_Answer_Engines_Literature_Review.pdf,Zero-Click Search and Answer Engines: A Literature Review on SEO Transformation in Digital Marketing,2025/2026,9,0.54,JIEM,https://ejurnal.kampusakademik.co.id/index.php/jiem/article/download/9040/7646/32415,零点击搜索与答案引擎综述
06_风险操纵,06_风险操纵_Adversarial_SEO_for_LLMs.pdf,Adversarial Search Engine Optimization for Large Language Models,2024,26,1.44,arXiv,https://arxiv.org/pdf/2406.18382,面向 LLM 的对抗式 SEO
06_风险操纵,06_风险操纵_CONFLICTBANK_Knowledge_Conflicts_in_LLMs.pdf,CONFLICTBANK: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLMs,2024,27,0.99,OpenReview,https://openreview.net/pdf?id=wjHVmgBDzc,知识冲突对模型回答的影响
06_风险操纵,06_风险操纵_Dynamics_of_Adversarial_Attacks_on_LLM_Search_Engines.pdf,Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines,2025,28,0.47,arXiv,https://arxiv.org/pdf/2501.00745,LLM 搜索引擎的对抗攻击动态
06_风险操纵,06_风险操纵_GASLITEing_the_Retrieval.pdf,GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-Based Search,2024/2025,39,3.24,OpenReview,https://openreview.net/pdf?id=LBd87fWerd,稠密向量检索的脆弱性
06_风险操纵,06_风险操纵_Manipulating_LLMs_to_Increase_Product_Visibility.pdf,Manipulating Large Language Models to Increase Product Visibility,2024,13,0.81,arXiv,https://arxiv.org/pdf/2404.07981,提升商品可见性的模型操纵风险
06_风险操纵,06_风险操纵_Persistent_Pre_Training_Poisoning_of_LLMs.pdf,Persistent Pre-Training Poisoning of LLMs,2024,17,0.91,arXiv,https://arxiv.org/pdf/2410.13722,预训练投毒带来的长期影响
06_风险操纵,06_风险操纵_PoisonArena_Competing_Poisoning_Attacks_in_RAG.pdf,PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation,2025,29,7.38,arXiv,https://arxiv.org/pdf/2505.12574,RAG 中竞争性投毒攻击
06_风险操纵,06_风险操纵_Ranking_Manipulation_for_Conversational_Search_Engines.pdf,Ranking Manipulation for Conversational Search Engines,2024,29,3.19,arXiv,https://arxiv.org/pdf/2406.03589v1.pdf,会话式搜索引擎排名操纵
06_风险操纵,06_风险操纵_StealthRank_LLM_Ranking_Manipulation.pdf,StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization,2025,31,1.29,arXiv,https://arxiv.org/pdf/2504.05804,隐蔽提示优化导致的排名操纵
06_风险操纵,06_风险操纵_Unveiling_Resilience_of_LLM_Enhanced_Search.pdf,Unveiling the Resilience of LLM-Enhanced Search Engines Against Adversarial Attacks,2026,12,1.06,arXiv,https://arxiv.org/pdf/2603.25500,LLM 增强搜索的抗攻击能力
07_垂直多模态,07_垂直多模态_Caption_Injection_Generative_Search_Engine.pdf,Caption Injection for Optimization in Generative Search Engines,2025,22,1.58,arXiv,https://arxiv.org/pdf/2511.04080,标题和图注注入对生成式搜索优化的影响
07_垂直多模态,07_垂直多模态_E_GEO_Ecommerce_Testbed.pdf,E-GEO: A Testbed for Generative Engine Optimization in E-Commerce,2025,22,0.92,arXiv,https://arxiv.org/pdf/2511.20867,电商场景下的 GEO 测试床
07_垂直多模态,07_垂直多模态_Multimodal_GEO_VLM_Rankers.pdf,Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers,2026,14,2.97,ResearchGate paper PDF,https://www.researchgate.net/publication/399931268_Multimodal_Generative_Engine_Optimization_Rank_Manipulation_for_Vision-Language_Model_Rankers,面向 VLM 排名器的多模态 GEO
07_垂直多模态,07_垂直多模态_Pinterest_GEO_VLM_Agent_Framework.pdf,Generative Engine Optimization: A VLM and Agent Framework for Pinterest Acquisition Growth,2026,11,1.70,arXiv,https://arxiv.org/pdf/2602.02961,Pinterest 增长场景中的 VLM 和 Agent 框架
07_垂直多模态,07_垂直多模态_Style_and_Semantic_Effects_Generative_Search_Engine.pdf,When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine,2025,59,2.54,ResearchGate paper PDF,https://www.researchgate.net/publication/395651066_When_Content_is_Goliath_and_Algorithm_is_David_The_Style_and_Semantic_Effects_of_Generative_Search_Engine,风格与语义因素对生成式搜索的影响