Research
Annotated papers that anchor the field's vocabulary — the foundational research behind GEO, read closely and distilled.
Editorial picks
GEO methods
- GEO: Generative Engine Optimization (Aggarwal et al. 2024)
Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande · KDD 2024 (Proc. 30th ACM SIGKDD) · 2024
The KDD 2024 paper that coined Generative Engine Optimization, released GEO-bench and the impression metric, and showed content rewrites can lift AI-answer visibility by up to 40% — with a GEO Wiki critique of how far that figure travels.
- What Evidence Do Language Models Find Convincing? (Wan et al. 2024)
Alexander Wan, Eric Wallace, Dan Klein · ACL 2024 Main (62nd ACM, Long Papers, pp. 7468–7484) · 2024
ACL 2024 paper, counterintuitive finding: choosing between conflicting web sources, LLMs react strongly to topical relevance and barely at all to credibility signals humans value — scientific references, neutral tone, formal citations. A GEO Wiki deep read with critique.
Further reading
How the engines work
- Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG (Singh et al. 2025)
- From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents (Zhang et al. 2025)
- A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges (Xi et al. 2025)
GEO methods
- Diagnosing and Repairing Citation Failures in Generative Engine Optimization (Tian et al. 2026)
- Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility (Liu & Xu 2026)
- Generative Engine Optimization: A VLM and Agent Framework for Pinterest Acquisition Growth (Zhang et al. 2026)
- EcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents (Ye et al. 2026)
- IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimization (2026)
- AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization (2026)
- Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers (Du et al. 2026)
- What Generative Search Engines Like and How to Optimize Web Content Cooperatively (Wu et al. 2025)
- StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization (Tang et al. 2025)
- White Hat Search Engine Optimization using Large Language Models (Bardas et al. 2025)
- E-GEO: A Testbed for Generative Engine Optimization in E-Commerce (2025)
- GASLITEing the Retrieval: Exploring Vulnerabilities in Dense Embedding-based Search (Ben-Tov & Sharif 2025)
- Role-Augmented Intent-Driven Generative Search Engine Optimization (Chen et al. 2025)
- Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines (Hu 2025)
- Ranking Manipulation for Conversational Search Engines (Pfrommer et al. 2024)
- Manipulating Large Language Models to Increase Product Visibility (Kumar & Lakkaraju 2024)
Measurement & evaluation
- Navigating the Shift: A Comparative Analysis of Web Search and Generative AI Response Generation (Chen et al. 2026)
- Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines (Zhang et al. 2025)
- PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation (Chen et al. 2025)
- Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses (Venkit et al. 2024)
- Evaluation of Retrieval-Augmented Generation: A Survey (Yu et al. 2024)