Peer Influence across Heterogeneous AI Models
Quick summary
arXiv:2610.03095v1 Announce Type: new Abstract: When two AI agents disagree, who persuades whom? As multi-agent systems increasingly combine language models of different families and sizes, the answer can determine which judgments survive interaction. Measuring persuasion as the probabilistic shift in an agent's decision after a single exchange with a dissenting peer, we test seven open-weight models across three language understanding tasks. We find that persuasion is strong: when models disagree, receivers often abandon their initial judgment after seeing a peer's answer and explanation. Sur
Key takeaways
- arXiv:2610.03095v1 Announce Type: new Abstract: When two AI agents disagree, who persuades whom?
- As multi-agent systems increasingly combine language models of different families and sizes, the answer can determine which judgments survive interaction.
- Measuring persuasion as the probabilistic shift in an agent's decision after a single exchange with a dissenting peer, we test seven open-weight models across three language understanding tasks.
Why it matters
“Peer Influence across Heterogeneous AI Models” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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