The paper introduces GEO Defender, a two‑stage defense system designed to protect generative search engines from malicious Generative Engine Optimization (GEO) attacks that rewrite web documents to manipulate generated answers. GEO Defender comprises a Shield Reranker, which learns a defensive residual to demote GEO‑rewritten documents while maintaining relevance, and a Training‑Free Shield Generation component that creates a natural‑language library guiding the target LLM’s source usage during inference. Experiments on both closed‑source and open‑source large language models show that GEO Defender dramatically lowers attack success rates from 50.32% to 6.20%, preserves over 94% of benign evidence usage, and maintains answer quality while generalizing to unseen attacks.
By Haozhang Li, Yangguang Shao, Xinjie Lin, Zhong Guan, Mi Zhou, Junzheng Shi
Counter‑GEO‑Bench is a new benchmark that evaluates how well defenses can stop large language models from producing misinformation when faced with generative engine‑optimized (GEO) content. It contains 247 human‑verified queries paired with both information‑preserving and information‑distorting GEO rewrites, and measures attack success rate, false positives, and answer quality across three victim LLMs. The study shows that existing off‑the‑shelf defenses reduce attack success by at most 5.7 %, while a lightweight baseline called C‑GEO Guard cuts success by 47.6 % with minimal loss of utility.
By Bing Zheng, Zongyao Zhao, Wenming Yang
Agent2UCB is a new agentic system designed for Generative Engine Optimization (GEO), which refines content to boost its likelihood of being cited or summarized by generative AI search engines. The system autonomously evaluates nine GEO strategies for each content item, selects the most effective one, and speeds up this selection using a bandit-based Agent2UCB policy that blends large language model priors with real-time reward signals. Additionally, it offers a lightweight, text-only SEO readiness check that assesses readability, topical coverage, and EEAT-style credibility, and experiments on GEO-Bench demonstrate consistent visibility gains while maintaining SEO quality.
By Sheldon Yu, Rui Wang, Tong Yu, Sungchul Kim, Doga Dogan, Junda Wu, Julian McAuley
arXiv:2605. 12887v2 Announce Type: replace-cross Abstract: Web-enabled LLM agents are changing how online information influences search outcomes.
By Hengwei Ye, Jiasheng Mao, Zhenhan Guan, Zheng Tian
The paper "Query Implied Generative Engine Optimization" introduces QI‑GEO, a method that infers user intent directly from documents to enhance visibility in Generative Search Engines. By approximating a document’s intent space, QI‑GEO identifies missing yet relevant content, improving objective scores by up to 15.9% and subjective scores by up to 17.6% on GEO‑Bench datasets. The approach yields nearly twice as many citation gains as losses, demonstrating that document‑derived intent approximations can boost content visibility without explicit query inputs.
By Shilpa Ramakrishna, William B. Andreopoulos
Counter‑GEO‑Bench is a new defense benchmark that tests how well systems can resist misinformation generated by generative engine optimization (GEO). It contains 247 human‑verified queries paired with both information‑preserving and information‑distorting GEO rewrites, and evaluates defenses on attack success rate, false positives, and answer quality across three large language models. Existing off‑the‑shelf defenses reduce attack success by at most 5.7%, while a lightweight baseline, C‑GEO Guard, cuts it by 47.6% with minimal loss of utility.
arXiv:2605. 29107v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly rank products, documents, and recommendations for user queries, which makes manipulating these rankings a growing concern for fairness and information integrity.
By Ojas Nimase, Zhe Chen, Gengpei Qi, Yue Zhao, Xiyang Hu
arXiv:2510. 11560v2 Announce Type: replace-cross Abstract: The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response.
By Elisabeth Kirsten, Jost Grosse Perdekamp, Qinyuan Wu, Mihir Upadhyay, Krishna P. Gummadi, Muhammad Bilal Zafar
arXiv:2607. 20730v1 Announce Type: cross Abstract: Large language models increasingly use search tools to retrieve up-to-date information, introducing a new attack surface in which retrieved documents can be manipulated.
By Zhaoqi Wang, Zijian Zhang, Xiaomei Yuan, Pengtao Kou, Jiamou Liu, Zhen Li, Liehuang Zhu
arXiv:2602. 12187v2 Announce Type: replace-cross 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.
By Sunghwan Kim, Wooseok Jeong, Serin Kim, Sangam Lee, Dongha Lee
arXiv:2609.05766v1 Announce Type: cross
Abstract: The dominant approach to building domain-specific pretraining corpora is to filter large web archives such as CommonCrawl. This works well for popula...
By Chirag Garg, Eelaaf Zahid, Farhan Ahmed, Jay Pankaj Gala, Eric Butler, Heiko Ludwig
arXiv:2608. 08994v1 Announce Type: cross Abstract: Retrieving relevant evidence from noisy web data is challenging, particularly in sensitive domains containing incomplete reports, heterogeneous language, and irrelevant content.
By Joshua Castillo, Santosh Nukavarapu, Ravi Mukkamala