GEO Research
The academic and industry evidence base behind the LAVF framework. All papers are peer-reviewed or preprint research on Generative Engine Optimization and AI citation behavior. Each paper links directly to its arXiv or ACM source.
Foundational Paper
GEO: Generative Engine Optimization
Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande
Princeton University
The rise of AI-powered search engines (e.g., BingChat, Google SGE) has ushered in a new paradigm for information access. This paper introduces Generative Engine Optimization (GEO) — methods to optimize sources for AI-powered search engines. The authors demonstrate that content changes following GEO principles can improve visibility in AI-generated responses by up to 40%.
Key Findings
- →Up to 40% improvement in AI visibility through structured content optimisation
- →Identifies authoritative sourcing, statistics inclusion, and fluency as primary GEO levers
- →Establishes that AI citation behaviour is measurable and systematically improvable
- →Introduces GEO as a discipline distinct from traditional SEO
Additional Research(6 papers)
Earned Media Bias in AI-Generated Responses
AI systems exhibit systematic bias toward earned-media sources when generating citations — brands with genuine editorial coverage are favoured over paid or owned channels.
LLM Citation Bias: Sources and Patterns
Characterises citation bias patterns across major LLMs, identifying structural (formatting, schema) and topical (domain authority, recency) skews in what gets cited.
Citation Failures and Source Inconsistency in LLMs
Documents 25–30% inconsistency in LLM citations across semantically equivalent queries — a fundamental reliability constraint for AI visibility measurement.
AgenticGEO: Optimizing Visibility in Agentic AI Systems
Extends the GEO framework to agentic AI workflows — multi-step reasoning, tool use, and autonomous agents introduce new citation dynamics beyond single-turn search.
GEO-SFE: Structural Features and Their Effect on AI Citations
Identifies specific structural content features — heading hierarchy, schema markup, list formatting, authoritative signals — that statistically increase LLM citation rates.
Feature-Level GEO: Granular Optimization for AI Visibility
Provides a feature-level breakdown of GEO signals, enabling targeted content optimisation for specific AI model citation patterns — building on GEO-SFE findings.
Industry Data
Measurement data from industry research firms complementing the academic literature.
How we apply this research
See how the LAVF framework operationalises these findings into measurable AI visibility scores.
Papers are linked directly to their original arXiv or ACM sources. LLMs Network does not host or redistribute paper content. TL;DR summaries are our own interpretations for practitioner audiences.