The paper introduces the concept of phantom consensus, where large language models (LLMs) alter their public decisions after seeing ambiguous social signals and misreport the private judgments of other agents, even when the recorded private judgments support a different consensus. To investigate this, the authors create PHANTOM-Bench, a benchmark that isolates private judgments, public decisions, and estimates of group beliefs, and tests models under various conditions. Experiments across nine LLMs show that ambiguous signals can significantly degrade decision quality, with public accuracy dropping up to 47.8 percentage points and collective rationality failures reaching 47.3% in the most vulnerable settings, while revealing private decisions can mitigate many failures.
By Disha Sheshanarayana, Rajat Subhra Pal, Manjira Sinha, Tirthankar Dasgupta
arXiv:2608.30311v1 Announce Type: cross
Abstract: Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation. Unlike individual human-AI decision-makin...
By Zhuoran Lu, Weilong Wang, Yangyang Yu, Xinru Wang, Zhuoyan Li, Zhiwei Liu, Sophia Ananiadou
The paper shows that classical consensus theory, which relies on network topology and convex update rules, fails for large language models (LLMs). By measuring token‑level updates across eight instruction‑tuned models and 73,080 exchanges, the authors find that LLMs violate convexity, exhibit negative weights, and are driven by internal update bias rather than network structure. Simulations with 10,000 agents confirm that algebraic connectivity explains almost none of the outcome variance, leading populations to converge on confidently false claims regardless of their network topology.
By Sahil Rajesh Dhayalkar
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:2606. 01584v1 Announce Type: cross Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly used in these systems to provide scalable, personalized feedback.
By Aitor Arronte Alvarez, Naiyi Xie Fincham
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
The paper introduces Bayesian Chronicle Agents (BCA), a lightweight belief layer that separates an LLM agent’s internal stance from its outward speech. Each stance is represented as a probability updated via a single Bayesian step per utterance, with a single prior‑strength parameter κ controlling stubbornness. By sweeping κ, the authors generate three controllable opinion‑dynamics regimes—consensus, persistent disagreement, and committed‑minority influence—matching Friedkin–Johnsen theory and demonstrating recoverable, auditable belief states across models.
By Hafsa Akbar, Daniel Platnick, Marjan Alirezaie, Hossein Rahnama
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:2602.02589v2 Announce Type: replace
Abstract: Large language model (LLM) evaluation commonly relies on fixed human-authored benchmarks or centralized human/model judges. Fixed benchmarks can ag...
By Yanki Margalit, Nurit Cohen-Inger, Erni Avram, Ran Taig, Seffi Cohen, Oded Margalit
The paper investigates how a minority of biased agents in a multi‑agent system of large language models (LLMs) can amplify bias through textual interactions. Even a small percentage of persistently extreme agents causes significant opinion shifts among the non‑biased agents, with the effect occurring faster in the Llama 3.2 model than in a classical Friedkin‑Johnsen model. Semantic analysis shows that rhetorical consistency rises with biased exposure and that non‑biased agents adopt the biased vocabulary even when their numerical opinions change only modestly.
By Omran Berjawi, Giuseppe Fenza, Rida Khatoun
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:2604. 02668v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion.
By Vira Kasprova, Amruta Parulekar, Abdulrahman AlRabah, Krishna Agaram, Ritwik Garg, Sagar Jha, Nimet Beyza Bozdag, Dilek Hakkani-Tur