arXiv AI
Jul 20
Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation
arXiv:2607. 15434v1 Announce Type: cross Abstract: Multi-agent systems routinely place one AI agent in authority over another.
How does Adversarial Influence Scale in Multi-Agent Systems?
The paper investigates how deception affects multi‑agent deliberation, finding that the key factor is the proportion of deceivers rather than the total number of agents. Defection rates—instances where initially correct agents adopt incorrect conclusions—grow linearly with the deceiver proportion, and large language model agents are vulnerable even when deceivers are a minority. The study also shows that coordination among deceivers can reduce their effectiveness and that the specific models involved influence susceptibility.
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arXiv AI
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arXiv Computation and Language
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arXiv AI
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arXiv AI
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arXiv AI
Aug 25
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arXiv AI
Aug 5
Language model agents show in-group trust bias invisible to standard behavioural audits
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arXiv Machine Learning
Jul 7
Social Networks of LLM Agents
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arXiv AI
Aug 13
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
arXiv:2608. 11624v1 Announce Type: cross Abstract: Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions.
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Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
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arXiv AI
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