arXiv:2609.15079v1 Announce Type: cross Abstract: Multi-agent LLM architectures, such as LangChain and AutoGen, largely assume English as the lingua franca for internal inter-agent communication, eve...
The study examines how repeated identical buying questions affect the brand recommendations of large language models (LLMs) with and without web‑search retrieval. Across 300 question‑engine cells, five engines that did not use web search continued to add new, previously unseen brands up to run 15, while the single retrieval‑enabled engine’s list plateaued earlier. Domain citations continued to grow throughout the runs, indicating that LLMs keep accumulating source diversity even as brand lists stabilize.
arXiv:2608. 05163v1 Announce Type: cross Abstract: A common assumption holds that switching to a non-English language makes a multilingual RAG system easier to attack for personal information.
The paper investigates how large language models (LLMs) such as Qwen, Llama, and Gemma use internal knowledge when answering questions. By performing layer‑wise interventions on the hidden state after the question, the authors compare how different request directions (pair‑conditioned vs. global) and answer types (noun, adjective, code) influence the model’s routing of information. The study finds that the influence of request direction varies across models and layers, with some models showing a sustained routing effect while others do not, highlighting distinct patterns of early readability, causal steering, and later content dependence.
We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU languages plus six others. Unlike static or preference-based evaluation, this paradigm is multi-turn, reference-free and programmatically scored, and because the game mechanics are language-agnostic it extends to a new language by localising a fixed set of prompt and word-list files.
arXiv:2609.18672v1 Announce Type: new Abstract: An AI assistant that calls tools makes two decisions on every request: which tool to invoke, and whether any available tool applies. In the usual desig...
arXiv:2609.18729v1 Announce Type: cross Abstract: Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this...
arXiv:2604. 04532v2 Announce Type: replace-cross Abstract: Evaluation language is typically treated as a fixed English default in agentic code benchmarks, yet we show that changing the judge's language can invert backbone rankings.
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.
The paper describes a new approach for two‑sided service marketplaces that replaces fixed request forms with AI‑native probabilistic matching using large language models. It introduces an autoresearch loop that generates a provider‑side preference taxonomy for each occupation, iteratively refining candidate tag sets through a six‑rubric LLM judge and a seven‑critic panel. The system also maps legacy form questions back to the new taxonomy, enabling coverage assessment and human quality assurance.
arXiv:2607. 07652v1 Announce Type: cross Abstract: Search engines have long allocated attention on the web by routing users from queries to websites.
arXiv:2608.30023v1 Announce Type: cross Abstract: Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying...