mlsanityData to 14 Oct 2026

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

arXiv 2606.23057 ↗First posted 22 Jun 2026cs.IR cs.CL cs.CYcited by 4

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Abstract (arXiv)

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 February and September 2026 (3,614 and 3,750 scored answers). Category Inclusion Rate, Recommendation Share, Competitive Vacuum Index and Co-Mention Asymmetry have stated denominators. February inclusion rates sit close together across an industry's sampled brands (mean Gini 0.30), while at least one brand is named in 80% or more of answers to 204 of 250 queries. Vacuums occur in 7.6% of queries; provisional open-vocabulary model readings suggest most reflect the sampled brand list. The partially pre-specified September replication shows strong cross-date Recommendation Share correlation (Spearman 0.994), unchanged vacuum prevalence and agreement of 60.8% against February's 57.2%. The descriptive size association persists. Fixed margins do not account for all co-mention structure: 31 ordered pairs depart from the September null. Agreement exceeds the query-independent null, and both parametric-pair advantage intervals are positive. All ten clustering seeds yield zero emergent clusters. Matcher validation remains provisional pending author checks. The statistics describe system output and identify no causal mechanism.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 22 Jun 2026.

Builds on

10 of 27 references.

References · 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026How Large Language Models Source Brand Reputation Across Languages and Marketscited by 3
  2. 2026The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendationscited by 2
  3. 2026Who Gets Named: Citation Type Predicts Individual Naming by Grounded Language Models, and a Roster Instrument Captures 0.5% of Itcited by 0
  4. 2026Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answerscited by 0