# GEO-Flag: Detecting and Measuring GEO-Optimized Web Content

arXiv: https://arxiv.org/abs/2608.16824 · HTML: https://mlsanity.com/paper/2608.16824/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [GEO: Generative Engine Optimization](https://mlsanity.com/paper/2311.09735/) (2023)
- [Adversarial Search Engine Optimization for Large Language Models](https://mlsanity.com/paper/2406.18382/) (2024)
- [ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search](https://mlsanity.com/paper/2006.05324/) (2020)
- [IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval](https://mlsanity.com/paper/2503.04644/) (2025)
- [What Generative Search Engines Like and How to Optimize Web Content Cooperatively](https://mlsanity.com/paper/2510.11438/) (2025)
- [C-SEO Bench: Does Conversational SEO Work?](https://mlsanity.com/paper/2506.11097/) (2025)
- [FinSight: Towards Real-World Financial Deep Research](https://mlsanity.com/paper/2510.16844/) (2025)
- [E-GEO: A Testbed for Generative Engine Optimization in E-Commerce](https://mlsanity.com/paper/2511.20867/) (2025)
- [Role-Augmented Intent-Driven Generative Search Engine Optimization](https://mlsanity.com/paper/2508.11158/) (2025)
- [Characterizing Web Search in The Age of Generative AI](https://mlsanity.com/paper/2510.11560/) (2025)
- [SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents](https://mlsanity.com/paper/2606.28356/) (2026)
- [How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews](https://mlsanity.com/paper/2604.27790/) (2026)
- [GEO-Bench: Benchmarking Ranking Manipulation in Generative Engine Optimization](https://mlsanity.com/paper/2605.29107/) (2026)

## Built on by (most cited first, 2 of 2)

- [A Layer Importance Metric for Quantization Accounting for the Speed-Quality Trade-off in Autoregressive Models](https://mlsanity.com/paper/2608.26926/) (2026)
- [An Empirical Measurement of Jailbreaking Evaluators](https://mlsanity.com/paper/2609.10594/) (2026)

## Abstract (arXiv)

Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines. This can give strategically optimized pages visibility disproportionate to their authority or relevance and even make weak or false information appear well supported. Unlike conventional search, generative search synthesizes information into direct answers rather than presenting competing sources, which can further amplify these risks, as assessing source provenance and authority requires additional user interaction. Despite these concerns, systematic methods for detecting GEO-optimized webpages remain underexplored. We introduce textttGEOFlagBench, a benchmark of 3,200 web content instances spanning 400 queries, four domains, and eight GEO optimizer families, and use it to systematically evaluate existing GEO detection methods. Although the strongest baseline achieves an aggregate F1 of 0.880, method-level and authorship-conditioned evaluations reveal substantial weaknesses and potential reliance on authorship-related shortcuts. We therefore propose Intervention-Paired Training (IPT), which supervises detector responses to GEO interventions and non-GEO AI polishing; on ModernBERT, IPT improves F1 from 0.862 to 0.944 and worst-group accuracy from 0.725 to 0.883. We develop a GEO-gated Agent system for auditing the Source Tier and verifiability of Citation URLs in detected GEO pages. Finally, we deploy the complete pipeline on released Google Search and Gemini-grounded retrieval results for 1,000 real-user queries. Across 10,095 available pages, we estimate an overall GEO prevalence of 8.90%, reaching 16.36% among pages modified in 2026. Our results establish a foundation for systematically detecting, auditing, and measuring GEO in real-world search ecosystems.