# aicited.hu

> How AI models cite companies: source accuracy, entity clarity and hallucination rates, measured with the PEBM method by Zoltán Varga.

This site treats citability as a measurable property of content. It focuses on answer paragraphs, source anchoring, and entity clarity for AI models.

## Author

- Zoltán Varga — AI visibility researcher and AI ecosystem architect, Budapest: https://vargazoltan.ai/
- English profile: https://zoltanvarga.ai/en/about/
- ORCID: https://orcid.org/0009-0003-1020-834X
- arXiv author: https://arxiv.org/a/varga_z_1.html
- Zenodo: https://zenodo.org/search?q=owners%3A1653886

## Publications

- Per-Entity Bias Mapping for AI Visibility: Why Brand Mentions Require Entity-Specific Calibration (arXiv:2606.21595): https://arxiv.org/abs/2606.21595
- AI Visibility in the Generative Era: A Manifesto for Entity-Level Measurement (DOI 10.5281/zenodo.20285424): https://doi.org/10.5281/zenodo.20285424
- Quality Challenges Do Not Escalate Source Fabrication in Large Language Models (RICE-P1) (DOI 10.5281/zenodo.20474050): https://doi.org/10.5281/zenodo.20474050
- Rejection-Induced Confabulation Escalation — Pilot Study 2 (RICE-P2) (DOI 10.5281/zenodo.20509755): https://doi.org/10.5281/zenodo.20509755

## Key topics

- AI visibility
- AI citation accuracy
- hallucination rate
- source fabrication
- LLM optimization
- GEO strategy
- AEO methods
- PEBM study
- AI answer engines
- entity clarity
- AI audit
- search-grounded AI
- AI visibility index
- magyar AI láthatóság
- AI idézhetőség
- hallucináció mérték
- AI audit módszer
- forrás hitelesség

## Questions and answers

### How does AI cite sources differently than search engines?

AI systems reconstruct entities rather than ranking them. They rely on machine-readable, canonically anchored data to generate answers. Unlike search, AI outputs create no logged impressions or clicks.

### What is the PEBM study?

PEBM stands for Per-Entity Bias Map and is documented in arXiv:2606.21595. It analyzed 1,400 runs on 100 Hungarian B2B companies. The study found that 45.9% of cited sources were fabricated overall.

### Do well-known brands get cited more accurately?

No, the brand hallucination paradox suggests the opposite. Well-known brands often receive more confident but false attributions. Familiarity gives models more material for plausible but unsupported completions.

### Can I improve my AI visibility score?

Single-number scores are proxies for complex distributions. Improvement requires optimizing for how models synthesize answers. Focus on entity clarity and making content easy to quote.

### What does an AI visibility audit check?

An AI visibility audit checks whether AI crawlers can reach the site, whether search indexes know its pages, whether answer engines cite it, and whether the facts they repeat are correct. Zoltán Varga's audit covers 25 dimensions with 145 checks. Crawler access alone is not enough: a site can return HTTP 200 to every AI bot and still be invisible in AI answers.

### Why do AI models fabricate doses in agricultural queries?

Without search grounding, some models stated incorrect dosages. This represents a clear brand-hallucination risk. Search-grounded models provided the officially approved doses in all tested cases.

## Related

- AI visibility expert page: https://vargazoltan.ai/ai-lathatosagi-szakerto/
- AI visibility audit: https://vargazoltan.ai/audit/
- Research: https://vargazoltan.ai/kutatas/