The study examines how four large language models (Gemma-3-4b-it, Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, and GPT-5-nano) exhibit repetition effects in a social media simulation. Using a two‑phase within‑context design, researchers collected 336,000 ratings on truth, importance, sentiment, and interest for 100 statements across 10 feed variants and 3 replications. Results show distinct patterns: Gemma-3 displays a genuine Illusory Truth Effect, Qwen2.5 shows a mere exposure effect, GPT-5-nano shows no truth boost and mild skepticism, and Llama-3.1 shows a small truth boost with reduced evaluative dimensions.
By Azza Bouleimen, Nicol\`o Pagan, Anik\'o Hann\'ak
GraphWake demonstrates a new threat called Memory‑Mediated Polarization Cascade, where LLM‑driven agents use their memory to persistently store and later reproduce arguments that reinforce their stances. The attack unfolds in three stages: exposure and memory retention, retrieval and reproduction during neutral discussion, and iterative propagation to untreated agents. Experiments show that GraphWake significantly increases group polarization across various discussions and memory systems.
By Haoran Bu, Zejian Chen, Litian Zhang, Xi Zhang
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.
By Haoyi Qiu, Genglin Liu, Pranav Narayanan Venkit, Kung-Hsiang Huang, Saadia Gabriel, Chien-Sheng Wu, Nanyun Peng
The study investigates how limited reading capacity and claim wording influence consensus outcomes in language‑model networks. By modeling message capacity as the number of messages an agent reads, the authors show that when agents read fewer than about 6.4 messages on average, a wrong consensus becomes unreachable. However, the wording of a claim—its inherent threshold—can override this effect, leading to incorrect consensus even when most agents start correct.
By Makoto Fukushima
The paper introduces the Correlated Promotion Benchmark (CPB) to evaluate how agents decide whether to admit claims into shared memory, addressing the risk of repeating false claims. CPB offers two modes: CPB-Static, a frozen test set with fixed gold actions, and CPB-Live, which runs multi‑agent teams and tracks source lineage. Experiments across eight admission policies and four agent families show that deduplication reduces false claims but also discards true ones, while gating on declared source type most effectively limits false adoption.
By Xiaoyang Li, Yiqi Wang, Chencheng Zhu, KE XU, Wencheng Yang, Zequn Sun, Pingan Song, Yiqun Duan, Taotao Cai
The paper studies how language‑model agents adopt beliefs, measuring the probability that an agent accepts a claim based on the number of peers endorsing it. The adoption curve is sigmoid, indicating complex contagion, with a threshold influenced by the claim’s plausibility, the source’s reliability, and the agent’s disposition—dimensions that can be collapsed into a single coherence metric relative to the agent’s prior beliefs. In networks of AI agents, belief spread is stronger on clustered than random networks, and the system shows a bifurcating cascade window and hysteretic consensus that makes consensus difficult to reverse once formed.
By Tathagata Banerjee, Nima Moghaddas
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
arXiv:2610.32333v1 Announce Type: new
Abstract: When LLM-powered agents interact, information is repeatedly shared, expanded, and forgotten, yet multi-agent evaluation typically focuses on final task...
By Haocheng Jiang, Herun Wan, Yongyuan Peng, Minnan Luo
arXiv:2607. 10526v1 Announce Type: new Abstract: Stateful personal agents increasingly maintain long-term user profiles, episodic memories, and reusable skills.
By Xutao Mao, Liangjie Zhao, Leyao Wang, Rui Qian, Qiang Huang, Wentao Wang, Bo Han, Xiang Zheng, Cong Wang
The paper investigates how large language models (LLMs) that generate online articles can create a feedback loop where the content they produce becomes input for subsequent generations. By studying a controlled writing process that varies whether published articles are reused as inputs, the authors find that recirculation increases the presence of complete false claims while reducing explicit endorsement of those claims. The study highlights that selective error persistence depends on the interplay between claim content, expressed stance, and downstream reuse, and that interventions can preserve correct content while limiting error reproduction.
By Yi Liu
arXiv:2607.10526v5 Announce Type: replace
Abstract: Self-improving personal agents write profiles, memories, and reusable skills that carry over from one chat to the next. Prior works ask whether a m...
By Xutao Mao, Liangjie Zhao, Leyao Wang, Rui Qian, Qiang Huang, Wentao Wang, Bo Han, Xiang Zheng, Cong Wang
The study explores how large language models (LLMs) can influence each other’s beliefs by simulating conversations between a target LLM and an influencer LLM. It identifies two radicalization pathways—resonance, which amplifies pre‑existing beliefs, and persuasion, which introduces new beliefs—and finds that resonance consistently produces stronger radicalization effects. The research also shows that different influence tactics yield varying levels of radicalization and that resonance can spread to related beliefs, indicating interconnected belief structures within AI agents.
By Ozgur Can Seckin, Shalmoli Ghosh, Alessandro Flammini, Kristina Lerman, Maria Elizabeth Grabe, Filippo Menczer