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.
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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.
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 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.
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: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.
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.