9 papers
Repeated Queries Exhaust an LLM's Brand Recommendations but Not Its Sources
Dmitrij Żatuchin
Whether repeated identical buying questions exhaust a language model's brand recommendations depends on retrieval. Across 300 question-engine cells (50 questions, six engines, 15 r…
The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendations
Dmitrij Żatuchin
Background: Researchers increasingly use repeated identical prompts to audit stochastic variation in large language model (LLM) brand recommendations, yet no standardized protocol…
The Language of the Question Selects the Market: Query Language and Exit IP as Separable Factors in Commercial Recommendations from a Generative Search Interface
Dmitrij Żatuchin
When a generative search interface answers a commercial question, which market's products it names is decided before the model reasons about the products. We report a controlled pr…
Demand-Side Measurement for Generative Engine Optimization: Constructing and Validating a Million-Persona, Intent-Annotated Buyer Corpus
Dmitrij Żatuchin, Daniil Dzemesjuk
Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying how brands enter or fail to en…
Who Gets Named: Citation Type Predicts Individual Naming by Grounded Language Models, and a Roster Instrument Captures 0.5% of It
Dmitrij Żatuchin
Prior work on AI brand visibility measures the firm: does a model recommend a company, and does that track its reputation. This study asks the question one level down, in categorie…
Where Does the Noise Come From? A Variance-Components Decomposition of Non-Determinism in LLM Brand Answers
Dmitrij Żatuchin
Teams measuring whether large language models (LLMs) recommend a brand face a reproducibility problem: ask the same question twice and the answer moves. Practice resamples each pro…