SecondVoice is a mixed‑reality system that lets participants speak up in co‑located discussions via an embodied virtual proxy, separating the content of a point from the speaker’s identity. Users input their intent through a structured process, and the system reformulates and vocalizes it into the conversation. In a study with 16 participants, the proxy channel led to more points being voiced than an anonymous text board and sparked multi‑turn engagement that the text board did not elicit.
By Yue Shen, Rehema Abulikemu, Ryan P. McMahan, Yan Chen
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'...
By Duofeng Xu, Bryan Hooi, Dandan Qiao
The paper investigates whether large language model (LLM) agents can simulate public deliberation by reflecting population opinion patterns and producing interaction-driven opinion change. Using census‑grounded Korean personas debating real policy questions, the study finds that persona agents fail to reliably reproduce population opinion patterns, often concentrating responses and reversing demographic differences. While deliberations generate reasoned, reciprocal arguments and some stance movement, much of this change occurs without peer exchange, and anchoring agents to population‑informed starting positions suppresses updating, indicating that population representation, argument generation, and interaction‑driven opinion change do not necessarily align.
By Chaemin Jang, Junsik Min, Jaewoo Choi, Donggyu Lee, Haiin Lee, Junyoung Park, Namhee Kim, Hyunwoo Kim, Jungwon Kim, Juho Kim, Nuri Kim, Jihee Kim
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.
By Anvesh Rao Vijjini, Sagar Manjunath, Snigdha Chaturvedi
arXiv:2606. 08172v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate high-stakes interactions in finance, medicine, and mental-health support, yet users have limited control over how these systems communicate.
By Manuele Reani, Hongjian Zhang, Hongyu Tian
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.
By Esra D\"onmez, Agnieszka Falenska
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...
By Lin Chen, Yitong Chen, Yong Li
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.
By Jared Moore, Noah Goodman, Nick Haber, Max Kleiman-Weiner
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.
By Esra D\"onmez, Agnieszka Falenska
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.
By Ljubisa Bojic, Tijana Stanic, Joerg Matthes, Agariadne Dwinggo Samala, Bojana Dinic, Jue Wang
The study audits 576 LLM-based social simulations from 350 papers using the PIMMUR framework, which evaluates agent profile, interaction, memory, minimal control, unawareness, and realism. Results show that PIMMUR principles are met more often than minimal control, unawareness, and realism, with frontier LLMs correctly identifying the underlying experiment in 65.2% of cases and half of prompts pre‑determining outcomes. Reproducing five experiments revealed that many reported collective phenomena disappear or reverse when PIMMUR principles are enforced, suggesting that apparent emergent behaviors may be methodological artifacts rather than genuine social dynamics.
By Jiaxu Zhou, Jen-tse Huang, Xuhui Zhou, Man Ho Lam, Xintao Wang, Hao Zhu, Wenxuan Wang, Maarten Sap
arXiv:2607. 25140v1 Announce Type: new Abstract: This paper studies the behavior of language models in a multi-agent crowd simulation, focusing on how affect propagates among agents that perceive and appraise one another.
By Funda Durupinar