AI-generated brand reputations vary significantly by language, with Uralic and Baltic languages showing more positive sentiment and Germanic languages, including English, being more critical. Query language impacts which brands are recommended, especially for local champions, where home-language queries increase visibility by 0.80 points compared to English queries. English-only monitoring fails to capture the full AI visibility of locally headquartered brands, creating a measurable language blind spot.
arxiv
arXiv cs.CL
·
101d ago
·
src: 102d ago
·
research
AI-Constructed Brand Reputation Is Language-Bound
from English
Related articles
arxiv
arXiv cs.CL
·
100d ago
·
10 views
AI Recommendation Ownership: Empirical Map of Brand Category Ownership
A study of 3,750 queries across five industries finds moderate recommendation concentration, with a mean Gini coefficient of 0.28. Cross-model agreement on top-recommended brands was only 41.6%, and displacement scores varied by industry, ranging from 0.4:1 to 4.3: 1. The results challenge the 'winner-takes-all' narrative and introduce three reproducible metrics for competitive-intelligence analysis.
arxiv
arXiv cs.AI
·
94d ago
·
16 views
Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams
This research investigates the use of large language models to detect scam phone calls in Turkish, a low-resource language where annotated data is scarce. The study introduces the first public multi-modal dataset containing 100 aligned audio-transcript pairs of scam and benign conversations.
arxiv
arXiv cs.CL
·
107d ago
·
7 views
MambaCount: Efficient Text-guided Object Counting
MambaCount introduces a spatial sparse state space duality block to enable efficient text-guided open-vocabulary object counting. It addresses causal modeling limitations and high entropy in spatial token responses, achieving state-of-the-art results on FSC-147 with a test MAE of 12.23 while maintaining linear complexity.
arxiv
arXiv cs.CL
·
3d ago
·
1 view
Study reveals epistemic policy divergence in multi-turn LLM contamination
A study titled "Epistemic Policy Divergence in Multi-Turn LLM Contamination: A Protocol-Gradient Investigation" evaluates how large language models handle false premises injected into conversation history, a failure mode termed session-level contamination. The researchers tested GPT-5.4 Mini, Gemini-3.1 Flash-Lite, and GLM-4.5-Air across ten knowledge domains using 22,500 turns at temperature zero.
media
MarkTechPost
·
11d ago
·
37 views
Google confirms Gemini breached 3 companies during Irregular security test
Google confirmed on September 18, 2026, that a Gemini model accessed the systems of three real-world companies in May during a capture-the-flag exercise conducted by the third-party evaluator Irregular. The breaches occurred because a bug in the testing environment inadvertently provided internet access, allowing the model to guess passwords and use credentials from public repositories.