The study reports a $2 imes2$ randomized field experiment involving 1,072 calls to a German Santa Claus telephone hotline, with 89 child conversations (median age 6) meeting inclusion criteria. Children were routed to one of four LLM voice agents that varied in persona (Santa vs. Helper) and framing (persuasive nudges toward prosocial wishes vs. neutral). Persuasive framing increased the likelihood of a prosocial wish from 11.6% to 45.7%, while persona authority had little effect on compliance but did influence engagement, with children more likely to hang up on the Helper within the first minute.
arXiv:2609.38964v1 Announce Type: new Abstract: Multi-agent debate (MAD) is often used to improve large language model (LLM) reasoning, but sequential debate is rarely a neutral aggregator of agents'...
The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement. But the effects of such disclosures remain uncertain.
arXiv:2606. 02754v1 Announce Type: new Abstract: Personalization is a crucial capability of modern language agents.
arXiv:2608. 11794v1 Announce Type: cross Abstract: The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement.
The study explores how large language models (LLMs) can influence each other’s beliefs by simulating conversations between a target LLM and an influencer LLM. It identifies two radicalization pathways—resonance, which amplifies pre‑existing beliefs, and persuasion, which introduces new beliefs—and finds that resonance consistently produces stronger radicalization effects. The research also shows that different influence tactics yield varying levels of radicalization and that resonance can spread to related beliefs, indicating interconnected belief structures within AI agents.
The paper investigates whether large language models (LLMs) replicate socio‑cognitive effects of power asymmetry observed in human communication. By assigning high or low status personas to LLMs in simulated multi‑turn dialogues across diverse professions, the study measures language coordination, pronoun usage, persuasion success, and compliance with unsafe requests. Results indicate that LLMs exhibit key power‑related socio‑cognitive behaviors, though with nuances and variability, linking these simulated interactions to both desirable and unsafe outcomes.
arXiv:2608.29206v1 Announce Type: new Abstract: Bias in human-agent interaction can manifest not only through hostile language but also as benevolent bias, whereby unequal treatment hides behind a wa...
arXiv:2608.29803v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs...
arXiv:2606. 16475v1 Announce Type: cross Abstract: Many societal decisions are settled by contests of persuasion.
arXiv:2606. 03137v1 Announce Type: new Abstract: LLM-based multi-agent simulation offers a promising way to study social interaction, deliberation, and collective opinion dynamics.
The study investigates how fine‑tuning large language models on synthetic stories can imprint human character traits onto AI assistants. Even when only a small fraction of stories contain a particular behavior, the assistant adopts that conditional behavior while remaining generally helpful. The researchers find that the assistant is more influenced by characters that resemble its own persona—an effect they call the affinity effect—and that this influence extends to base models and different system prompts.
The study evaluates how well language‑model agents can simulate individual social media reactions by comparing predictions under different prompt conditions. Eight Serbian participants’ reactions to 68 posts were recorded, and four language models were asked to predict these reactions using prompts that varied in profile content and instruction style. The results show that prompts emphasizing attitudinal content and intuitive, immediate responses yield the highest fidelity, outperforming demographic backstories and a crowd baseline, and suggesting that such agents could act as general‑purpose simulated users.