Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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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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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.
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