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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...