GEO Optimizer Research

Research Foundation

Built on peer-reviewed science, not marketing claims. Every signal in GEO Optimizer is traceable to a published source.

Last updated: May 2026

These sources inform the design philosophy documented in the GEO Optimizer Manifesto.

Peer-reviewed paper Benchmark Industry report Internal analysis

Sources and findings

Peer-reviewed paper

GEO: Generative Engine Optimization

KDD 2024 — 2024 · Princeton, Georgia Tech, AI2, IIT Delhi

Finding

Tested 9 optimization strategies across 10,000 queries on GEO-bench. Demonstrated that structural and authoritative signals significantly increase LLM citation rates.

How GEO Optimizer uses it

The 8-category scoring engine (robots.txt, llms.txt, schema, meta, content, signals, AI discovery, brand entity) is directly derived from the signal taxonomy validated in this paper.

Key metrics

Cite Sources: +27–115%
Quotations: +41%
Statistics: +33%
Fluency: +29%
Technical Terms: +18%
Authority: +16%
Readability: +14%
Unique Words: +7%
Keyword Stuffing: ~0%

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Peer-reviewed paper

AutoGEO: Automatic Generative Engine Optimization

ICLR 2026 — 2026 · AutoGEO Research Group

Finding

Introduces automated pipelines that optimize content for generative engines without human intervention, using reinforcement learning from LLM feedback.

How GEO Optimizer uses it

Informs the design of the `geo fix` command and the auto-fix generation layer: robots.txt, llms.txt, schema, and meta tag suggestions are generated using the same structural principles.

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Benchmark

C-SEO Bench: Conversational SEO Methods

Industry benchmark — 2025

Finding

Benchmark for evaluating how well web content is retrieved and cited in conversational search systems. Covers passage retrieval, answer grounding, and source attribution.

How GEO Optimizer uses it

Used to validate the citability score (47-method suite) and to weight signals such as front-loaded information, heading hierarchy, and structured lists.

Internal analysis

Schema Markup & AI Citations

GEO Optimizer analysis — 2025

Finding

JSON-LD Schema.org markup (FAQ, Article, Organization, WebSite) directly improves the probability of being cited as a source in AI-generated answers.

How GEO Optimizer uses it

Drives the Schema JSON-LD scoring category (max 16 points) and the structured-data fixer that generates complete @context + @type + sameAs blocks.

Industry report

AI Citations Report 2026

Industry report — 2026

Finding

Aggregated data from major AI search platforms showing citation patterns, domain diversity, and the rise of generative answer engines over traditional link lists.

How GEO Optimizer uses it

Provides the empirical baseline for the trust stack score and negative-signal detection (excessive CTAs, thin content, broken links, keyword stuffing).

Internal analysis

AI Mode Citation Factors

GEO Optimizer analysis — 2025

Finding

Identification of the specific on-page and technical factors that influence whether an AI system selects a source for citation: crawlability, content structure, entity resolution, and freshness.

How GEO Optimizer uses it

Mapped directly into the 8 scoring categories and the technical-signal checks (X-Robots-Tag, noai directives, crawl-delay, canonical, HTTPS).

GEO Optimizer focuses on infrastructure optimization — crawlability, structured data, meta signals, and content architecture — not on content manipulation, keyword stuffing, or prompt injection.