collaborators

13 papers

cs.IR2026

Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answers

Dmitrij Żatuchin

Counts of which brands a large language model names are useful only if they hold long enough to act on. We test this with a pre-registered replication. The same 162 requests (54 qu…

cs.IR2026

A Shared Taste for Model-Written Text: The Generator-by-Selector Matrices of "AI-AI Bias" Show No Detectable Own-Model Premium

Dmitrij Żatuchin

Laurito et al. (PNAS 2025) showed that large language models choosing between two descriptions of the same product, paper or film prefer the description written by a language model…

cs.IR2026

System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer

Dmitrij Żatuchin

Audits of AI visibility summarise the brand recommendations of deployed language models into per-system profiles. We test whether such a profile describes the system on one corpus…

cs.IR2026

Ask a Language Model for Lottery Numbers: Concentration in Repeated Six-of-49 Outputs

Dmitrij Żatuchin

We evaluate six language-model configurations on requests for six distinct random integers from 1-49. Across 1,200 attempted calls using four English prompt variants, 1,184 respons…

cs.IR2026

Measuring Brand and Source Discovery under Repeated LLM Queries: A Finite-Sample Audit

Dmitrij Żatuchin

Repeated-query audits must distinguish recovery of a collected set from completeness of possible outputs. We apply sample-based rarefaction to 4,500 responses from 50 buying questi…

cs.IR2026

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…

cs.IR2026

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…

cs.IR2026

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…

cs.IR2026

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…

cs.IR2026

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…

cs.IR2026

How Large Language Models Source Brand Reputation Across Languages and Markets

Dmitrij Zatuchin

When a large language model (LLM) answers a question about a company, it grounds the answer in retrieved web sources, and those sources decide what the model says. Most analysis of…

cs.IR2026

The Language Blind Spot: How Query Language and Brand Recognition Tier Shape AI-Constructed Brand Reputation Across Twelve European Languages

Dmitrij Żatuchin

Large language models (LLMs) increasingly mediate how people form impressions of organisations, yet most monitoring is done in English, assuming an English query returns a represen…

cs.IR2026

Who Owns the AI Recommendation? A Multi-Industry Empirical Map of Brand Category Ownership Across Large Language Models

Dmitrij Żatuchin

This exploratory study measures brand inclusion across five industries, 50 brands and 250 queries, each put five times to GPT-5.2, Gemini 3 Flash and Perplexity sonar-pro in Februa…