What Gets Cited: Competitive GEO in AI Answer Engines
AI answer engines generate answers from retrieved pages but cite only a few sources. This makes visibility depend not just on ranking, but on being cited. We study competitive Generative Engine Optimization (GEO): when two retrieved candidates compete, what makes one more likely to be cited first? We build a controlled two-document retrieval-augmented generation (RAG) testbed that injects exactly two candidate sources into the model context and measures which source is referenced by the first citation marker in the output. Across six LLMs we execute 252,000 trials, repeated paired comparisons under one factorial program over 18 content factors. In each trial the two sources differ in exac...
| Authors | Rahul Vishwakarma, Shushant Kumar, Ratnesh Jamidar |
|---|---|
| Primary category | cs.AI |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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