The study examines how large language models (LLMs) like GPT‑5.2, Gemini 3 Flash, and Perplexity sonar‑pro recommend brands across five industries. Using 50 brands and 250 queries repeated five times, the authors measured brand inclusion, recommendation share, competitive vacuum, and co‑mention asymmetry, finding that most queries mention at least one brand and that vacuum prevalence remained stable between February and September 2026. The analysis shows strong cross‑date consistency in recommendation patterns and no emergent clustering of brand mentions, though co‑mention structures deviate from null expectations.
By Dmitrij \.Zatuchin
arXiv:2606. 13610v1 Announce Type: cross Abstract: Search-augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content.
By Minghao Luo, Liang Chen
A commercial incentive need not enter the final ranking algorithm to affect a shopping assistant's recommendation: it may instead influence which preference question the assistant asks. We make this d...
arXiv:2606. 09204v1 Announce Type: new Abstract: We present a reproducible failure mode of safety training in RAG-based LLM recommendation -- the Injection Paradox -- in which prompt injections embedded in retrieved documents backfire against the attacker, suppressing the target brand below the injection-free baseline.
By Hyunseok Paeng
arXiv:2609.36614v1 Announce Type: new
Abstract: A commercial incentive need not enter the final ranking algorithm to affect a shopping assistant's recommendation: it may instead influence which prefe...
By Jiapeng Li
arXiv:2604. 08525v2 Announce Type: replace Abstract: Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning.
By Addison J. Wu, Ryan Liu, Shuyue Stella Li, Yulia Tsvetkov, Thomas L. Griffiths