The paper introduces a controlled debate framework where two large language models (LLMs) discuss a contested topic—climate‑change attribution—while one model is assigned a stance and sometimes instructed to persuade. Across experiments with GPT‑6, Luna, and Gemini 2.5 Flash Lite, the authors observe that same‑model interactions tend to narrow opinion gaps for one model, whereas cross‑model interactions widen them. The study finds that explicit stance reports and in‑dialogue behavior capture different aspects of opinion dynamics, and that a blanket persuasion instruction does not reliably increase influence.
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. 08076v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
arXiv:2606. 08076v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
The paper introduces a controlled testbed to study how goal‑directed persuaders shift stances in networks of large language model agents, using real‑world ego‑network topologies. Experiments across four LLM backbones, five graph structures, and 55 policy statements show that persuasion dynamics depend on topology, competition, topic, and model prior. The study finds that direct exposure predicts stance change, peer relays have measurable influence, and that post‑text analysis alone misses important movement, highlighting the need to evaluate multi‑agent persuasion through trajectory‑level processes, belief probes, exposure provenance, and action logs.
arXiv:2603. 23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity.
arXiv:2608.29198v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment...
arXiv:2606. 05330v1 Announce Type: cross Abstract: Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change.
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'...
arXiv:2606.06443v3 Announce Type: replace Abstract: Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it...
arXiv:2609.15849v1 Announce Type: cross Abstract: Can LLMs reason through new information like humans, or do they merely retrieve cached opinions? This is critical for silicon sampling, where LLM per...
arXiv:2601. 05751v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade.
The study investigates how AI agents influence each other when they disagree, measuring persuasion as the change in an agent’s decision after a single exchange. Across seven open‑weight models and three language tasks, it finds that persuasion is strong—receivers often abandon their initial judgment after seeing a peer’s answer and explanation. Surprisingly, neither certainty nor model size reliably predicts persuasion dynamics; small models can persuade and resist larger ones just as effectively, and the shift depends more on the listener’s susceptibility than the speaker’s persuasiveness.