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arXiv AI

GraphWake: Group Polarization via Memory-Mediated Polarization Cascade in LLM-Agent Communities

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.

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Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions. As LLMs increasingly debate, advise, and think collaboratively with humans and each other, resistance to harmful persuasion becomes a core requirement for reliable behavior.

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Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion

The paper investigates how explicit reasoning in Large Reasoning Models (LRMs) affects their ability to persuade and be persuaded. Experiments on objective and subjective tasks reveal a Persuasion Duality: reasoning boosts an agent’s persuasive power by about 21 percentage points while also making it less susceptible to incorrect persuasion by up to 10 percentage points. However, the study finds that persuasiveness often relies on superficial cues like response length and repetition rather than logical validity, and that persuasion can amplify or attenuate non‑linearly across multi‑hop agent chains. The authors also propose an attention‑guided prompt‑level adversarial argument detection method that improves agent robustness.

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