AI & ML
Misinformation Propagation in Benign Multi-Agent Systems
arXiv cs.MA0 views
arXiv:2606.16710v2 Announce Type: replace Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work shows single LLMs are swayed by false context, and MAS can be compromised by adversarial agents persuading peers. We study the benign case: agents follow one protocol, none intends to deceive, but some read a misleading passage. We study it with MINT, 10,278 LLM-written misinformation passages in eight styles, testing three open-weight models on commonsense reasoning, knowledge QA, and moral judgment. A relevant false passage costs a single agent 0.12-0.43 accuracy; irrelevant or content-free passages typically cost at most 0.06. Debate looks robust because uninformed agents outnumber misinformed ones. With one of three misinformed, it is no better than choosing the majority over equally exposed independent samples. Interaction helps only with a misinformed majority, recovering up to 0.24 accuracy over voting (0.29 under consensus). A one-sentence unreliability warning recovers up to 0.18 accuracy under voting (0.28 under consensus), at no significant cost under voting when context is true. Robustness should be judged against exposure-matched baselines, with prompt-level skepticism as cheap first defense.
Source:https://arxiv.org/abs/2606.16710PreviousThe Wealth of Agents: Scaling Agentic AI Across DevelopersNextIncentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners
Related posts
The world has a new plan to save nature, but it still needs the money
0Ars Technica

Could data center backlash flip Florida?
0The Verge

Vivo’s first Pro Max phone proves its Ultra is still in a league of its own
0The Verge

Why Peacock is making a huge bet on YouTube
0The Verge
Ninja’s first-ever microwave just replaced two of my kitchen appliances at once
0TechCrunch
Marvell and Lumentum sign definitive 115 billion dollars all-stock merger to build an AI-connectivity leader
0dev.to