多智能体推理中引入运行时监控，防止错误传播提升决策可靠性
Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
- 各智能体先独立作答，再共享推理过程并修正答案
- 实验显示该机制能提高准确率，减少错误传递
- 适用于网络安全、网络通信等多领域，适合高可靠性场景
多智能体人工智能系统通过让不同语言模型交换推理路径、修正初始判断并协同决策，可提升答案选择的准确性。然而，这种交互也可能带来可靠性风险：一个智能体的推理可能纠正另一个的错误，但也可能误导原本正确的智能体。本文研究通过推理交换与运行时答案修正实现可靠的多智能体通信。我们构建了一个框架，让智能体先独立回答多项选择题，随后共享推理过程并修改判断。在数值实验中，评估了该过程是否提升准确率、产生正向而非负向的答案转变，并在网络安全、网络通信和通用知识等不同领域验证其有效性。结果揭示了多智能体推理在何种情况下增强可靠性，以及何时可能传播错误。
原文摘要 · Abstract (English)
Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision. However, such communication may also introduce reliability risks: reasoning from one agent can correct another agent's mistake, but it can also mislead an agent that was initially correct. This paper studies reliable multi-agent AI communication through reasoning exchange and runtime answer revision. We develop a framework in which agents first answer multiple-choice questions independently, then share reasoning traces and revise their decisions. We conduct numerical experiments where we evaluate whether this process improves accuracy, produces more positive than negative answer transitions, and remains effective across domains such as cybersecurity, networking, and general knowledge. The results help identify when multi-agent reasoning improves reliability and when it may propagate errors.
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