Searcharxiv⌕ Search

arXiv · 2610.00253

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

Abstract

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 of 6,475 stored responses (6,324 analysable) collected between December 2025 and February 2026 from five deployed endpoints across gift-recommendation, corporate-reputation and category-ownership queries. The collection harness cut many answers short: 83.1% of Gemini 3 Flash answers in category ownership end mid-sentence under a 1,024-token output cap. With every answer cut to its first 800 characters, a character n-gram classifier cross-validated by prompt attributes one response to GPT-5.2, Gemini 3 Flash, Gemini 3 Flash with search, Grok or Perplexity sonar-pro with 97.84% accuracy (5,028 responses, 383 prompts, majority class 31.5%, 30 split seeds). Length alone falls to the majority rate, 24 formatting statistics reach 95.79%, and masking brand names and capitalised tokens leaves 97.72%. Held-out query conditions keep 97.43% weighted by size and 88.0% unweighted; in a retrieval-grounded arm that changes the harness, no Grok answer is attributed to Grok (0/120). Aggregated into 50 model-by-domain-by-condition units, twelve behavioural features separate four systems at 66.53% under grouped cross-validation, against a label-permutation null with mean 33.71% and 95th percentile 46.0%. Across domains the aggregate profile fails: a forest trained on category-ownership units assigns all 22 gift units to the wrong system, consistent with a reversal in brand volume (8.41 against 0.94 brands per response in gifts, 3.01 against 3.91 in category ownership), while single responses transfer at 89.92% balanced accuracy. The surface form of an answer carries the system across the query domains tested; aggregated brand behaviour does not, and the uncrossed design cannot separate the system from the domain or the harness.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dmitrij Żatuchin. 2026-09-23. System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer. https://arxiv.org/abs/2610.00253

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

LLMRec: Benchmarking Large Language Models on Recommendation Task

Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. However, the application of LLMs in the recommendation domain has not been thoroughly investigated. To bridge this gap, we propose LLMRec, a LLM-based recommender system designed for benchmarking LLMs on various recommendation tasks. Specifically, we benchmark several popular off-the-shelf LLMs, such as ChatGPT, LLaMA, ChatGLM, on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization. Furthermore, we investigate the effectiveness of supervised finetuning to improve LLMs' instruction compliance ability. The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation. However, they demonstrated comparable performance to state-of-the-art methods in explainability-based tasks. We also conduct qualitative evaluations to further evaluate the quality of contents generated by different models, and the results show that LLMs can truly understand the provided information and generate clearer and more reasonable results. We aspire that this benchmark will serve as an inspiration for researchers to delve deeper into the potential of LLMs in enhancing recommendation performance. Our codes, processed data and benchmark results are available at https://github.com/williamliujl/LLMRec.

cs.IR↗

Ontology Interoperability: A Comprehensive Framework for Industrial-Scale Applications

Different ontologies with conflicting and overlapping concepts cause havoc in the design, develop, and deploy of ontology-driven applications. In this work, we propose a comprehensive ontology interoperability framework for industrial-scale ontology-driven applications. The framework employs three state-of-the-art semantic techniques in different phases of the ontology engineering (OE) life cycle: ontology design patterns (ODPs) in the design phase, ontology matching and versioning (OM\&OV) in the develop phase, and ontology validation (OVA) in the deploy phase, to achieve better ontology interoperability.

cs.IR↗

ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments

Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration. However, existing benchmarks offer limited coverage of research intents and insufficient support for scalable answer construction and reproducible multi-turn evaluation. We propose \textbf{ScholarQuest}, a taxonomy-guided benchmark constructed from over 1,000 computer science topics and organized around four research intents. To construct answer sets at scale, we combine multi-source retrieval, citation expansion, and multi-stage relevance adjudication. We further build \textit{\textbf{ScholarBase}}, a million-scale literature environment with unified retrieval and citation-expansion interfaces, together with a shared multi-turn search harness. We benchmark complete literature search systems and compare language models within this harness, evaluating retrieval coverage, search efficiency, and robustness across research intents. Results show that agentic methods outperform single-shot retrieval baselines, yet the best-performing search system achieves only 0.312 Recall@100 and 0.352 Recall@All. Comparisons under the shared harness reveal intent-dependent model strengths, with higher Planner token consumption not consistently yielding higher recall. We release the benchmark, retrieval backend, and evaluation harness for reproducible research

cs.IR↗