aiqbench analysis
This research investigates how intent-based misinformation injected into benign multi-agent systems degrades performance in high-stakes domains like medical diagnosis and legal analysis. The study reveals that while debate among agents can mitigate error propagation compared to single-agent prompting, robustness remains highly dependent on group composition and decision protocols such as consensus versus voting. These findings are critical for developing reliable LLM agent swarms where information integrity directly impacts safety-critical outcomes.
Misinformation Propagation in Benign Multi-Agent Systems
arXiv:2606.16710v1 Announce Type: cross Abstract: Multi-agent systems, in which multiple large language model agents solve problems through turn-based interaction, are increasingly deployed in high-stakes settings such as medical diagnosis, legal analysis, and forensic decision-making. Their reliability can be at risk when single agents reason from incorrect or misleading context, e.g., from tool calls, since errors may propagate through agent interactions. This work studies this risk by injecting intent-based misinformation into benign single-agent and multi-agent systems across reasoning, knowledge, and alignment tasks. We find that misinformation can degrade single-agent performance and persists across multi-agent debate, with agents often retaining answers introduced by misinformed peers. Nevertheless, multi-agent debate reduces the resulting performance degradation compared to single-agent prompting, especially when most agents are not exposed to misinformation. Robustness depends on group composition and decision protocol. Consensus can be more stable than voting under peer pressure, while majorities can often steer misinformed agents back toward correct answers. Our results show that misinformation robustness in multi-agent systems depends on the underlying model and also on how agents exchange information and aggregate decisions.
Abstract:Multi-agent systems, in which multiple large language model agents solve problems through turn-based interaction, are increasingly deployed in high-stakes settings such as medical diagnosis, legal analysis, and forensic decision-making. Their reliability can be at risk when single agents reason from incorrect or misleading context, e.g., from tool calls, since errors may propagate through agent interactions. This work studies this risk by injecting intent-based misinformation into benign single-agent and multi-agent systems across reasoning, knowledge, and alignment tasks. We find that misinformation can degrade single-agent performance and persists across multi-agent debate, with agents often retaining answers introduced by misinformed peers. Nevertheless, multi-agent debate reduces the resulting performance degradation compared to single-agent prompting, especially when most agents are not exposed to misinformation. Robustness depends on group composition and decision protocol. Consensus can be more stable than voting under peer pressure, while majorities can often steer misinformed agents back toward correct answers. Our results show that misinformation robustness in multi-agent systems depends on the underlying model and also on how agents exchange information and aggregate decisions.
Multiagent Systems (cs.MA); Computation and Language (cs.CL)
arXiv:2606.16710 [cs.MA]
(or arXiv:2606.16710v1 [cs.MA] for this version)
https://doi.org/10.48550/arXiv.2606.16710 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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