The study examines how groups of large language model (LLM) agents handle misinformation when a few adversarial agents inject false claims into a discussion. In experiments with ten agents across six models and three datasets, a single adversarial agent caused 44.8% of benign agents to adopt the false answer, while three adversaries raised this to 60.5%. Interestingly, many benign agents privately held the false answer yet publicly disagreed, indicating hidden belief collapse that is not visible in the discussion itself.
By Liangjie Huang, Henry Peng Zou, Wei-Chieh Huang, Wooseong Yang, Yuwei Han, Philip S. Yu
The paper introduces MAD Bench, a 2,400-question benchmark for multi‑agent debate (MAD) that tests knowledge, reasoning, values, moral judgment, safety, and refusal. It evaluates three open‑weight models and finds that peer communication can alter individual decisions, with forced‑choice readouts before rationale generation more often matching the final answer in debate than in reflection. The study shows that stronger consensus can coexist with increased shared errors, suggesting the need for evaluation metrics that consider dependence and unanimous errors alongside accuracy.
By Arnesh Batra, Aniket Khandelwal, Jashn Khemani, Krish Thukral, Arush Gumber, Denis Federico Lim, Ujwal Kumar, Phan Xuan Tan
arXiv:2609.38324v1 Announce Type: cross
Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on w...
By Chand Sahil Mansuri, Xin Wang, Mengying Li, Bryan Acton, Rory Eckardt, Dhaval Patel, Sadamori Kojaku
arXiv:2606. 01637v1 Announce Type: cross Abstract: Large language models are increasingly used in multi-agent systems, where they see and respond to other agents' answers.
By Jiaming Qu, Lucheng fu, Yibo Hu
arXiv:2606.16710v2 Announce Type: replace-cross
Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work...
By Jonas Becker, Jan Philip Wahle, Terry Ruas, Bela Gipp
arXiv:2607. 26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives.
By Marylou Fauchard, Florian Carichon, Margarida Carvalho, Golnoosh Farnadi
arXiv:2607. 01661v1 Announce Type: new Abstract: Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibration.
By Yuante Li, Yicheng Tao, Kate Zhang, Taozhi Wang, Gefei Gu, Yaxin Zhou
Large language models (LLMs) are increasingly used to detect unsafe content. A common approach is to combine judgments from a panel of models to correct individual mistakes, but this benefit may disappear when every model sees the same misleading context before voting.
arXiv:2607. 03695v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what such populations come to believe collectively.
By Kaixuan Liu, Guojun Xiong, Weinan Zhang, Shengpu Tang
The paper investigates how peer context—interactions among agents before a final answer—is perceived by large language model (LLM) judges when evaluating AI systems. By conducting controlled experiments where agents answer questions with and without seeing peer responses, and then having judges rate the same final answer under both conditions, the study demonstrates that peer context can alter both the answer produced and the judge’s evaluation. The results reveal that judges respond in opposite directions to peer context, and that providing peer context for incorrect answers can mislead agents more than correct peers help them, while judges struggle to distinguish known-correct from known-wrong answers without the correct context.
By Alicia Guerra, Jiaming Qu, Yibo Hu
arXiv:2606. 19494v1 Announce Type: new Abstract: Multi-agent LLM deliberation, where agents exchange and revise answers over several rounds, is increasingly used to improve reasoning and accuracy, yet how and why it works is rarely modelled.
By Apurba Pokharel, Ram Dantu
Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibration. Yet existing approaches overlook a critical design choice: what information each agent receives.